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SALib/SALib | numpy | 178 | delta indices not summing up to unity | Hey,
I am using the delta analysis for calculating sensetivity indices for s model with 21-30 Parameters. The delta values sum up to 1.2 - 4. As far as I understood the paper of Borgonovo 2007 they should sum up to 1, since the conditional denseties are normalized by the unconditional density. However, could it be that this is just the case if it is an additive model?
The calculated sensitivity indices seen okay and are less than one.
Thanks for helping out!
| closed | 2017-12-07T07:21:13Z | 2019-11-07T22:50:25Z | https://github.com/SALib/SALib/issues/178 | [
"question_interpretation"
] | witteire | 6 |
scikit-hep/awkward | numpy | 3,236 | [CPU/GPU] prod kernel on an empty list of a complex type gives a wrong result | ### Version of Awkward Array
master branch (2.6.7)
### Description and code to reproduce
```python
def test_block_boundary_prod_complex13():
rng = np.random.default_rng(seed=42)
array = rng.integers(50, size=1000)
complex_array = np.vectorize(complex)(
array[0 : len(array) : 2], array[1 : len(array) : 2]
)
content = ak.contents.NumpyArray(complex_array)
assert np.allclose(to_list(ak.prod(content, -1, highlevel=False)), np.prod(ak.Array(content)), equal_nan=True)
offsets = ak.index.Index64(np.array([0, 5, 996, 1000], dtype=np.int64))
depth1 = ak.contents.ListOffsetArray(offsets, content)
print(to_list(ak.prod(depth1, -1, highlevel=False)))
print([np.prod(ak.Array(depth1[0])), np.prod(ak.Array(depth1[1])), np.prod(ak.Array(depth1[2]))])
```
where `ak.Array(depth1[2])` has `ArrayType(NumpyType('complex128'), 0, None)`. The `ak.prod` result of an empty list should be `(1+0j)`, as correctly produced by Numpy:
```
[(6891360-24365880j), (nan+nanj), 0j]
[(6891360-24365880j), (nan+nanj), (1+0j)]
``` | open | 2024-09-12T13:05:31Z | 2024-09-12T13:07:30Z | https://github.com/scikit-hep/awkward/issues/3236 | [
"bug (unverified)"
] | ianna | 0 |
biolab/orange3 | numpy | 6,461 | Data Sets doesn't remember non-English selection | According to @BlazZupan, if one chooses a Slovenian dataset (in English version of Orange?) and saves the workflow, this data set is not selected after reloading the workflow.
I suspect the problem occurs because the language combo is not a setting and is always reset to English for English Orange (and to Slovenian for Slovenian), and thus the data set is not chosen because it is not shown.
The easiest solution would be to save the language as a schema-only setting. | closed | 2023-06-02T12:50:51Z | 2023-06-16T08:02:49Z | https://github.com/biolab/orange3/issues/6461 | [
"bug"
] | janezd | 0 |
KevinMusgrave/pytorch-metric-learning | computer-vision | 239 | Why precision_at_1 for a not trained model from MNIST example is 0.95 | For [TripletMarginLossMNIST example]( https://github.com/KevinMusgrave/pytorch-metric-learning/blob/master/examples/notebooks/TripletMarginLossMNIST.ipynb ) if measured before the training was started 'precision_at_1' is 0.953. Why is it so high? The model was not trained or pretrained on any dateset.
```
from pytorch_metric_learning import losses, miners, distances, reducers, testers
from pytorch_metric_learning.utils.accuracy_calculator import AccuracyCalculator
### MNIST code originally from https://github.com/pytorch/examples/blob/master/mnist/main.py ###
from torchvision import datasets
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np
### MNIST code originally from https://github.com/pytorch/examples/blob/master/mnist/main.py ###
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
return x
### MNIST code originally from https://github.com/pytorch/examples/blob/master/mnist/main.py ###
def train(model, loss_func, mining_func, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, labels) in enumerate(train_loader):
data, labels = data.to(device), labels.to(device)
optimizer.zero_grad()
embeddings = model(data)
indices_tuple = mining_func(embeddings, labels)
loss = loss_func(embeddings, labels, indices_tuple)
loss.backward()
optimizer.step()
if batch_idx % 20 == 0:
print("Epoch {} Iteration {}: Loss = {}, Number of mined triplets = {}".format(epoch, batch_idx, loss, mining_func.num_triplets))
### convenient function from pytorch-metric-learning ###
def get_all_embeddings(dataset, model):
tester = testers.BaseTester()
return tester.get_all_embeddings(dataset, model)
### compute accuracy using AccuracyCalculator from pytorch-metric-learning ###
def test(dataset, model, accuracy_calculator):
embeddings, labels = get_all_embeddings(dataset, model)
print("Computing accuracy")
accuracies = accuracy_calculator.get_accuracy(embeddings,
embeddings,
np.squeeze(labels),
np.squeeze(labels),
True)
print("Test set accuracy (MAP@10) = {}".format(accuracies["mean_average_precision_at_r"]))
print("Test set accuracy (MAP@1) = {}".format(accuracies["precision_at_1"]))
device = torch.device("cuda")
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
batch_size = 256
dataset1 = datasets.MNIST('.', train=True, download=True, transform=transform)
dataset2 = datasets.MNIST('.', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1, batch_size=256, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset2, batch_size=256)
model = Net().to(device)
optimizer = optim.Adam(model.parameters(), lr=0.01)
num_epochs = 10
### pytorch-metric-learning stuff ###
distance = distances.CosineSimilarity()
reducer = reducers.ThresholdReducer(low = 0)
loss_func = losses.TripletMarginLoss(margin = 0.2, distance = distance, reducer = reducer)
mining_func = miners.TripletMarginMiner(margin = 0.2, distance = distance, type_of_triplets = "semihard")
accuracy_calculator = AccuracyCalculator(include = ("mean_average_precision_at_r","precision_at_1",), k = 10)
### pytorch-metric-learning stuff ###
for epoch in range(1, num_epochs+1):
test(dataset2, model, accuracy_calculator)
#train(model, loss_func, mining_func, device, train_loader, optimizer, epoch)
``` | closed | 2020-11-27T13:34:48Z | 2020-11-27T16:20:48Z | https://github.com/KevinMusgrave/pytorch-metric-learning/issues/239 | [] | bransGl | 2 |
ultralytics/ultralytics | deep-learning | 19,104 | vid_stride when input is frame_list | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
When input is list of images (have been read with cv2.imread and put in a list ), model.predict(frame_list, vid_stride=n) not working. just predict in vid_stride=1 state. in other words, changing vid_stride to 2 or 3 is ignored. just work with 1.
### Additional
_No response_ | open | 2025-02-06T15:37:41Z | 2025-02-06T15:43:45Z | https://github.com/ultralytics/ultralytics/issues/19104 | [
"question",
"detect"
] | ansaricard | 2 |
aio-libs-abandoned/aioredis-py | asyncio | 778 | ConnectionResetError: [Errno 104] Connection reset by peer | Hi, everybody!
I use torando+aioredis, and recently i met this issue, below is traceback
my environ: aioredis==1.2.0 tornado==5.1.1
I use this method `aioredis.create_redis_pool(**args)` to create pool
can anybody show me help? thx a lot.
`Traceback (most recent call last):
File "/usr/local/lib/python3.6/site-packages/tornado/web.py", line 1699, in _execute
result = await result
File "/views/notice.py", line 341, in get
items, page_size, total_page, total_size = await Notice.cache_or_api_list(notice_id_list, page_count, page_size)
File "models/notice.py", line 136, in cache_or_api_list
items = await cls.query_list(page_list)
File "models/notice.py", line 92, in query_list
items = await asyncio.gather(*[Notice.cache_or_api(notice_id) for notice_id in notice_id_list])
File "models/notice.py", line 37, in cache_or_api
info = await redis.execute('get', redis_key)
File "models/notice.py", line 37, in cache_or_api
info = await redis.execute('get', redis_key)
File "models/notice.py", line 37, in cache_or_api
info = await redis.execute('get', redis_key)
[Previous line repeated 11 more times]
File "/usr/local/lib/python3.6/site-packages/aioredis/connection.py", line 183, in _read_data
obj = await self._reader.readobj()
File "/usr/local/lib/python3.6/site-packages/aioredis/stream.py", line 94, in readobj
await self._wait_for_data('readobj')
File "/usr/local/lib/python3.6/asyncio/streams.py", line 464, in _wait_for_data
yield from self._waiter
File "/usr/local/lib/python3.6/asyncio/selector_events.py", line 723, in _read_ready
data = self._sock.recv(self.max_size)
ConnectionResetError: [Errno 104] Connection reset by peer` | open | 2020-07-16T08:54:31Z | 2022-07-07T17:43:45Z | https://github.com/aio-libs-abandoned/aioredis-py/issues/778 | [
"bug"
] | zzlpeter | 58 |
Lightning-AI/pytorch-lightning | pytorch | 20,620 | test: `flaky test_results.py::test_result_reduce_ddp` terminated with signal SIGABRT | ### Bug description
The test **`tests/tests_pytorch/core/test_results.py::test_result_reduce_ddp`** fails intermittently, similar to the test addressed in [#20537](https://github.com/Lightning-AI/pytorch-lightning/pull/20537), with the error:
> `torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with signal SIGABRT`
To address this, the test could be marked **flaky**.
### **Background**
We are evaluating how our tool for **test prioritization** could find test failures faster in your project. During a CI rerun for commit `7322d63bef2cf1a0439f8b19b545cd4a89da62b0`, we encountered the failure of this test, which you can see in this log: [GitHub Actions Log](https://github.com/syncpr-user1/pytorch-lightning-random_order/actions/runs/13600237309/job/38024923746).
### What version are you seeing the problem on?
master
### How to reproduce the bug
```python
```
### Error messages and logs
```
# Error messages and logs here please
```
### Environment
<details>
<summary>Current environment</summary>
```
#- PyTorch Lightning Version (e.g., 2.5.0):
#- PyTorch Version (e.g., 2.5):
#- Python version (e.g., 3.12):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
```
</details>
### More info
_No response_ | closed | 2025-03-05T18:22:12Z | 2025-03-10T13:20:06Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20620 | [
"bug",
"needs triage",
"ver: 2.5.x"
] | kaiyaok2 | 0 |
ijl/orjson | numpy | 362 | Add support for __slots__ classes | Hi 👋
I'd like to ask if it would be possible to add support for [__slots__](https://docs.python.org/3/reference/datamodel.html#slots) python classes?
Currently trying to serialize one yields a `Type is not JSON serializable`
```python
class MySlotsClass:
__slots__ = ("a",)
def __init__(self, a):
self.a = a
```
the expected behaviour would be
```python
>>> orjson.dumps(MySlotsClass(42))
b'{"a":42}'
```
This type of class allows for major memory savings when dealing with many small objects.
| closed | 2023-03-16T13:34:18Z | 2023-03-20T23:03:58Z | https://github.com/ijl/orjson/issues/362 | [] | grzegorzme | 1 |
AirtestProject/Airtest | automation | 598 | windows系统在celery队列里执行 在终端看到命令都会执行两次 最后停止不动 请问这可能是什么原因呢 | [2019-11-08 16:37:50,870: INFO/MainProcess] Connected to redis://localhost:6379//
[2019-11-08 16:37:51,883: INFO/MainProcess] mingle: searching for neighbors
[2019-11-08 16:37:56,934: INFO/MainProcess] mingle: all alone
[2019-11-08 16:37:57,962: INFO/MainProcess] pidbox: Connected to redis://localhost:6379//.
[2019-11-08 16:37:59,952: INFO/MainProcess] celery@X9ZY4FKMMCJWZD6 ready.
[2019-11-08 16:37:59,956: INFO/MainProcess] Received task: task.tasks.wechat.subscribe_account[f5a9621d-149a-4a6c-85e9-ebe876a85c27]
[04:37:59][DEBUG]<airtest.core.android.adb> g:\soft\python3.6.5\lib\site-packages\airtest\core\android\static\adb\windows\adb.exe -s 127.0.0.1:21503 get-state
[2019-11-08 16:37:59,959: DEBUG/MainProcess] g:\soft\python3.6.5\lib\site-packages\airtest\core\android\static\adb\windows\adb.exe -s 127.0.0.1:21503 get-state
[04:38:00][DEBUG]<airtest.core.android.adb> g:\soft\python3.6.5\lib\site-packages\airtest\core\android\static\adb\windows\adb.exe -s 127.0.0.1:21503 wait-for-device
[2019-11-08 16:38:00,037: DEBUG/MainProcess] g:\soft\python3.6.5\lib\site-packages\airtest\core\android\static\adb\windows\adb.exe -s 127.0.0.1:21503 wait-for-device
[04:38:00][DEBUG]<airtest.core.android.adb> g:\soft\python3.6.5\lib\site-packages\airtest\core\android\static\adb\windows\adb.exe -s 127.0.0.1:21503 shell getprop ro.build.version.sdk
[2019-11-08 16:38:00,119: DEBUG/MainProcess] g:\soft\python3.6.5\lib\site-packages\airtest\core\android\static\adb\windows\adb.exe -s 127.0.0.1:21503 shell getprop ro.build.version.sdk
| closed | 2019-11-08T08:38:37Z | 2019-11-11T01:37:28Z | https://github.com/AirtestProject/Airtest/issues/598 | [] | hejiaqiang1980 | 1 |
Lightning-AI/pytorch-lightning | pytorch | 19,994 | Logging with Fabric using steps | ### Description & Motivation
Logging using Fabric does not consider any steps during training, unlike when using the Lightning Trainer. A LightningModule calling self.log simply passes the logged dictionary and nothing else to the Fabric logging code when using Fabric but when using the Trainer it is handled by grouping/frequency adjustments (such as aggregating during multi-gpu training or logging every X steps [default 50]).
### Pitch
An option to enable similar logging in Fabric as the Lightning Trainer. This could be off by default but could track steps that are submitted with fabric hooks/calls, such as:
`fabric.call('on_train_step')`
This would allow for logged values to be aggregated during the same step, which makes logs more readable.
### Alternatives
_No response_
### Additional context
_No response_
cc @borda | open | 2024-06-18T22:56:50Z | 2024-06-19T19:52:08Z | https://github.com/Lightning-AI/pytorch-lightning/issues/19994 | [
"feature",
"needs triage"
] | liambsmith | 2 |
fastapi/sqlmodel | sqlalchemy | 1,242 | how to config the pydantic JSON fields | ### Privileged issue
- [X] I'm @tiangolo or he asked me directly to create an issue here.
### Issue Content
| closed | 2024-12-12T02:00:36Z | 2025-02-28T01:37:07Z | https://github.com/fastapi/sqlmodel/issues/1242 | [] | cjdxhjj | 10 |
QuivrHQ/quivr | api | 2,790 | Parse celery config from env | Use pydantic settings to parse `.env` celery config | closed | 2024-07-01T13:51:57Z | 2024-10-04T16:06:18Z | https://github.com/QuivrHQ/quivr/issues/2790 | [
"Stale",
"area: backend"
] | linear[bot] | 2 |
yunjey/pytorch-tutorial | deep-learning | 90 | language model detach(states) | why shouldn't states be updated after every training step?I can't understand this line--"states = detach(states)" and what on earth does this step do? I am new to PyTorch and I am very grateful if anyone can help me.
```
# Some part of the code was referenced from below.
# https://github.com/pytorch/examples/tree/master/word_language_model
import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
from data_utils import Dictionary, Corpus
# Hyper Parameters
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epochs = 5
num_samples = 1000 # number of words to be sampled
batch_size = 20
seq_length = 30
learning_rate = 0.002
# Load Penn Treebank Dataset
train_path = './data/train.txt'
sample_path = './sample.txt'
corpus = Corpus()
ids = corpus.get_data(train_path, batch_size)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
# RNN Based Language Model
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
self.init_weights()
def init_weights(self):
self.embed.weight.data.uniform_(-0.1, 0.1)
self.linear.bias.data.fill_(0)
self.linear.weight.data.uniform_(-0.1, 0.1)
def forward(self, x, h):
# Embed word ids to vectors
x = self.embed(x)
# Forward propagate RNN
out, h = self.lstm(x, h)
# Reshape output to (batch_size*sequence_length, hidden_size)
out = out.contiguous().view(out.size(0)*out.size(1), out.size(2))
# Decode hidden states of all time step
out = self.linear(out)
return out, h
model = RNNLM(vocab_size, embed_size, hidden_size, num_layers)
model.cuda()
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Truncated Backpropagation
def detach(states):
return [state.detach() for state in states]
# Training
for epoch in range(num_epochs):
# Initial hidden and memory states
states = (Variable(torch.zeros(num_layers, batch_size, hidden_size)).cuda(),
Variable(torch.zeros(num_layers, batch_size, hidden_size)).cuda())
for i in range(0, ids.size(1) - seq_length, seq_length):
# Get batch inputs and targets
inputs = Variable(ids[:, i:i+seq_length]).cuda()
targets = Variable(ids[:, (i+1):(i+1)+seq_length].contiguous()).cuda()
# Forward + Backward + Optimize
model.zero_grad()
states = detach(states)#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
outputs, states = model(inputs, states)
loss = criterion(outputs, targets.view(-1))
loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), 0.5)
optimizer.step()
step = (i+1) // seq_length
if step % 100 == 0:
print ('Epoch [%d/%d], Step[%d/%d], Loss: %.3f, Perplexity: %5.2f' %
(epoch+1, num_epochs, step, num_batches, loss.data[0], np.exp(loss.data[0])))
# Sampling
with open(sample_path, 'w') as f:
# Set intial hidden ane memory states
state = (Variable(torch.zeros(num_layers, 1, hidden_size)).cuda(),
Variable(torch.zeros(num_layers, 1, hidden_size)).cuda())
# Select one word id randomly
prob = torch.ones(vocab_size)
input = Variable(torch.multinomial(prob, num_samples=1).unsqueeze(1),
volatile=True).cuda()
for i in range(num_samples):
# Forward propagate rnn
output, state = model(input, state)
# Sample a word id
prob = output.squeeze().data.exp().cpu()
word_id = torch.multinomial(prob, 1)[0]
# Feed sampled word id to next time step
input.data.fill_(word_id)
# File write
word = corpus.dictionary.idx2word[word_id]
word = '\n' if word == '<eos>' else word + ' '
f.write(word)
if (i+1) % 100 == 0:
print('Sampled [%d/%d] words and save to %s'%(i+1, num_samples, sample_path))
# Save the Trained Model
torch.save(model.state_dict(), 'model.pkl')`
``` | closed | 2017-12-24T13:11:10Z | 2020-10-09T18:53:01Z | https://github.com/yunjey/pytorch-tutorial/issues/90 | [] | qazwsx74269 | 2 |
ivy-llc/ivy | pytorch | 28,525 | Fix Frontend Failing Test: numpy - math.paddle.diff | To-do List: https://github.com/unifyai/ivy/issues/27497 | closed | 2024-03-09T20:56:31Z | 2024-04-02T09:25:05Z | https://github.com/ivy-llc/ivy/issues/28525 | [
"Sub Task"
] | ZJay07 | 0 |
vitalik/django-ninja | pydantic | 1,418 | Accept JSON as a payload field | Here I have this code that I want to get from user with PATCH method.
the issue is I always got "missing" error for this type of fields.
```python
class AnswerSchema(Schema):
key: str
value: str
class UserDisease(Schema):
question: str
answers: List[AnswerSchema]
class UserMedicalInSchema(Schema):
...
diseases: UserDisease = None
```
And this is the error:
```python
{
"detail": [
{
"type": "missing",
"loc": [
"form",
"payload",
"diseases",
"answers",
0,
"key"
],
"msg": "Field required"
},
{
"type": "missing",
"loc": [
"form",
"payload",
"diseases",
"answers",
0,
"value"
],
"msg": "Field required"
}
]
}
```
I sent the request from Ninja docs page(/api/docs) and always get this error for any field which is List input.
Also I get this error in my terminal(`manage.py runserver`) when change 'disease' in 'UserMedicalInSchema' to `disease = Optional[UserDisease] = None`
```python
File "/home/enriquette/Programming/Projects/work/sarvestan/sarvestan/urls.py", line 22, in <module>
from .v1.api import api as api_v1
File "/home/enriquette/Programming/Projects/work/sarvestan/sarvestan/v1/api.py", line 1, in <module>
from app_api.v1 import api as api_v1
File "/home/enriquette/Programming/Projects/work/sarvestan/app_api/v1/api.py", line 5, in <module>
from .user import router as user_router
File "/home/enriquette/Programming/Projects/work/sarvestan/app_api/v1/user.py", line 109, in <module>
@router.patch('medical', auth=JWTAuth(), response=UPDATE_RESPONSE, tags=['user'])
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/router.py", line 268, in decorator
self.add_api_operation(
~~~~~~~~~~~~~~~~~~~~~~^
path,
^^^^^
...<16 lines>...
openapi_extra=openapi_extra,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/router.py", line 319, in add_api_operation
path_view.add_operation(
~~~~~~~~~~~~~~~~~~~~~~~^
path=path,
^^^^^^^^^^
...<16 lines>...
openapi_extra=openapi_extra,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/operation.py", line 426, in add_operation
operation = OperationClass(
path,
...<16 lines>...
openapi_extra=openapi_extra,
)
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/operation.py", line 82, in __init__
self.signature = ViewSignature(self.path, self.view_func)
~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/signature/details.py", line 87, in __init__
self.models: TModels = self._create_models()
~~~~~~~~~~~~~~~~~~~^^
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/signature/details.py", line 144, in _create_models
flatten_map = self._args_flatten_map(args)
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/signature/details.py", line 181, in _args_flatten_map
for name, path in self._model_flatten_map(arg.annotation, arg.alias):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/signature/details.py", line 205, in _model_flatten_map
yield from self._model_flatten_map(field.annotation, name) # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/enriquette/Programming/Projects/work/sarvestan/.venv/lib/python3.13/site-packages/ninja/signature/details.py", line 201, in _model_flatten_map
for attr, field in model.model_fields.items():
^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.13/typing.py", line 1365, in __getattr__
return getattr(self.__origin__, attr)
File "/usr/lib/python3.13/typing.py", line 548, in __getattr__
raise AttributeError(item)
AttributeError: model_fields
```
and this is the API.
```python
@router.patch('medical', auth=JWTAuth(), response=UPDATE_RESPONSE, tags=['user'])
def update_user_medical_info(
request,
payload: Form[schema.UserMedicalInSchema] = None,
):
user = request.user
if payload:
data = checks.normalize_medical_info(payload.dict())
check_result = checks.medial_info(data)
if check_result != 200:
return schema_gen.error_response(400, *check_result)
for field, value in data.items():
setattr(user, field, value)
user.save()
return 204, None
return schema_gen.error_response(400, 'no data provided')
``` | closed | 2025-03-08T11:50:03Z | 2025-03-10T15:39:21Z | https://github.com/vitalik/django-ninja/issues/1418 | [] | smjt2000 | 3 |
huggingface/datasets | nlp | 6,534 | How to configure multiple folders in the same zip package | How should I write "config" in readme when all the data, such as train test, is in a zip file
train floder and test floder in data.zip | open | 2023-12-26T03:56:20Z | 2023-12-26T06:31:16Z | https://github.com/huggingface/datasets/issues/6534 | [] | d710055071 | 1 |
keras-team/keras | python | 20,376 | AttributeError: 'Functional' object has no attribute 'get_state_tree' | using keras 3.4.1 in a colab. a simple model can be queried for `trainable_variables` and `non_trainable_variables`
but `get_state_tree()` fails on `Functional` objects?
( `.compile` or `.build` don't make a difference )
```
import os
os.environ['KERAS_BACKEND'] = 'jax'
import keras
keras.__version__
```
```
3.4.1
```
```
from keras.layers import Input, Dense
from keras.models import Model
input = Input((10, 3))
foo = Dense(3)(input)
model = Model(input, foo)
model.trainable_variables, model.non_trainable_variables
```
```
([<KerasVariable shape=(3, 3), dtype=float32, path=dense/kernel>,
<KerasVariable shape=(3,), dtype=float32, path=dense/bias>],
[])
```
```
model.get_state_tree()
```
```
AttributeError: 'Functional' object has no attribute 'get_state_tree'
``` | closed | 2024-10-18T04:33:50Z | 2024-10-18T11:47:44Z | https://github.com/keras-team/keras/issues/20376 | [
"stat:awaiting response from contributor",
"type:Bug"
] | matpalm | 4 |
marimo-team/marimo | data-visualization | 3,893 | Datatypes showing up again in mo.ui.table | ### Describe the bug
After 0.11.6 there is a regression where datatypes are shown, even if you pass a list of dicts. This was apparently fixed in https://github.com/marimo-team/marimo/pull/2907, but is back again.
### Environment
{
"marimo": "0.11.6",
"OS": "Windows",
"OS Version": "11",
"Processor": "Intel64 Family 6 Model 140 Stepping 1, GenuineIntel",
"Python Version": "3.12.9",
"Binaries": {
"Browser": "--",
"Node": "--"
},
"Dependencies": {
"click": "8.1.8",
"docutils": "0.21.2",
"itsdangerous": "2.2.0",
"jedi": "0.19.2",
"markdown": "3.7",
"narwhals": "1.25.1",
"packaging": "24.2",
"psutil": "6.1.1",
"pygments": "2.19.1",
"pymdown-extensions": "10.14.3",
"pyyaml": "6.0.2",
"ruff": "0.9.4",
"starlette": "0.45.3",
"tomlkit": "0.13.2",
"typing-extensions": "4.12.2",
"uvicorn": "0.34.0",
"websockets": "11.0.3"
},
"Optional Dependencies": {
"pandas": "2.2.3"
},
"Experimental Flags": {}
}
### Code to reproduce
_No response_ | closed | 2025-02-24T14:42:18Z | 2025-02-24T20:48:38Z | https://github.com/marimo-team/marimo/issues/3893 | [
"bug"
] | mrdobalina2k | 1 |
Lightning-AI/pytorch-lightning | machine-learning | 20,250 | LearningRateMonitor broken on MPS backend with Apple silicon | ### Bug description
When the optimizer contains any data of type `float64`, then adding a `LearningRateMonitor` causes a Value Error on MPS backends with apple silicon. See the self-contained and minimal example in "How to reproduce the bug" below.
The error is:
```
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_monitor.py", line 219, in <dictcomp>
name: torch.tensor(value, device=trainer.strategy.root_device) for name, value in latest_stat.items()
TypeError: Cannot convert a MPS Tensor to float64 dtype as the MPS framework doesn't support float64. Please use float32 instead.
```
When removing the `LearningRateMonitor`, the code runs through, thus the optimiser itself is fine.
Note that the quick fix to remove the `lr=np.float64(0.01)` works only for the minimal example. In my case, the optimiser is imported from an external module and has more parameters, making it much harder to change.
I tried out 4 fixes in the pytorch lightning source code, all of them fix the problem, but might have side-effects or not work on other devices or in other configurations:
Replace `torch.tensor(value, device=trainer.strategy.root_device)` in [this line](https://github.com/Lightning-AI/pytorch-lightning/blob/f3f10d460338ca8b2901d5cd43456992131767ec/src/lightning/pytorch/callbacks/lr_monitor.py#L217) to one of:
- `torch.tensor(value, device="cpu")`
- `torch.tensor(value, device=value.device)`
- `torch.tensor(value, device=trainer.strategy.root_device, dtype=torch.float32)`
- `value`
### What version are you seeing the problem on?
v2.4
### How to reproduce the bug
```python
import numpy as np
import torch
from torch import nn
from torch.optim import Adam
from torch.utils.data import DataLoader, TensorDataset
import pytorch_lightning as pl
from pytorch_lightning.callbacks import LearningRateMonitor
class SimpleModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.layer = nn.Linear(2, 1)
def forward(self, x):
return self.layer(x)
def training_step(self, batch, batch_idx):
x, y = batch
loss = nn.functional.mse_loss(self(x), y)
return loss
def configure_optimizers(self):
optimizer = Adam(self.parameters(), lr=np.float64(0.01))
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
return [optimizer], [scheduler]
# Data
x = torch.randn(100, 2)
y = torch.randn(100, 1)
dataset = TensorDataset(x, y)
dataloader = DataLoader(dataset, batch_size=2)
# Training
model = SimpleModel()
lr_monitor = LearningRateMonitor(logging_interval='step')
trainer = pl.Trainer(max_epochs=10, callbacks=[lr_monitor])
trainer.fit(model, dataloader)
```
### Error messages and logs
```
Epoch 0: 98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 49/50 [00:00<00:00, 188.15it/s, v_num=13]Traceback (most recent call last):
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/test_lr_monitor.py", line 37, in <module>
trainer.fit(model, dataloader)
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 538, in fit
call._call_and_handle_interrupt(
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 47, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 574, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 981, in _run
results = self._run_stage()
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1025, in _run_stage
self.fit_loop.run()
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
self.advance()
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
self.epoch_loop.run(self._data_fetcher)
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
self.advance(data_fetcher)
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 233, in advance
call._call_callback_hooks(trainer, "on_train_batch_start", batch, batch_idx)
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 218, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_monitor.py", line 173, in on_train_batch_start
latest_stat = self._extract_stats(trainer, interval)
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_monitor.py", line 216, in _extract_stats
trainer.callback_metrics.update({
File "/Users/malteebnerlightly/Documents/GitHub/lightly-train/.venv/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_monitor.py", line 217, in <dictcomp>
name: torch.tensor(value, device=trainer.strategy.root_device) for name, value in latest_stat.items()
TypeError: Cannot convert a MPS Tensor to float64 dtype as the MPS framework doesn't support float64. Please use float32 instead.
Epoch 0: 98%|█████████▊| 49/50 [00:00<00:00, 129.92it/s, v_num=13]
```
### Environment
Machine is a MacBook Pro with M1-Pro CPU
<details>
<summary>Current environment</summary>
* CUDA:
- GPU: None
- available: False
- version: None
* Lightning:
- lightning-utilities: 0.11.7
- pytorch-lightning: 2.4.0
- torch: 2.4.1
- torchmetrics: 1.4.1
- torchvision: 0.19.1
* Packages:
- absl-py: 2.1.0
- aenum: 3.1.15
- aiohappyeyeballs: 2.4.0
- aiohttp: 3.10.5
- aiosignal: 1.3.1
- annotated-types: 0.7.0
- antlr4-python3-runtime: 4.9.3
- async-timeout: 4.0.3
- attrs: 24.2.0
- autocommand: 2.2.2
- backports.tarfile: 1.2.0
- certifi: 2024.7.4
- charset-normalizer: 3.3.2
- exceptiongroup: 1.2.2
- filelock: 3.15.4
- frozenlist: 1.4.1
- fsspec: 2024.9.0
- grpcio: 1.65.5
- huggingface-hub: 0.24.6
- hydra-core: 1.3.2
- idna: 3.8
- importlib-metadata: 8.0.0
- importlib-resources: 6.4.0
- inflect: 7.3.1
- iniconfig: 2.0.0
- jaraco.context: 5.3.0
- jaraco.functools: 4.0.1
- jaraco.text: 3.12.1
- jinja2: 3.1.4
- licenseheaders: 0.8.8
- lightning-utilities: 0.11.7
- markdown: 3.7
- markupsafe: 2.1.5
- more-itertools: 10.3.0
- mpmath: 1.3.0
- multidict: 6.0.5
- mypy: 1.11.1
- mypy-extensions: 1.0.0
- networkx: 3.3
- numpy: 2.1.1
- omegaconf: 2.3.0
- packaging: 24.1
- pillow: 10.4.0
- platformdirs: 4.2.2
- pluggy: 1.5.0
- protobuf: 5.27.3
- psutil: 6.0.0
- pydantic: 1.10.18
- pydantic-core: 2.20.1
- pydeprecate: 0.3.2
- pytest: 8.3.2
- pytest-mock: 3.14.0
- python-dateutil: 2.9.0.post0
- pytorch-lightning: 2.4.0
- pyyaml: 6.0.2
- regex: 2024.7.24
- requests: 2.32.3
- ruff: 0.6.1
- safetensors: 0.4.4
- setuptools: 74.1.2
- six: 1.16.0
- sympy: 1.13.2
- tensorboard: 2.17.1
- tensorboard-data-server: 0.7.2
- timm: 1.0.8
- tomli: 2.0.1
- torch: 2.4.1
- torchmetrics: 1.4.1
- torchvision: 0.19.1
- tqdm: 4.66.5
- typeguard: 4.3.0
- types-tqdm: 4.66.0.20240417
- typing-extensions: 4.12.2
- urllib3: 2.2.2
- werkzeug: 3.0.3
- wheel: 0.43.0
- yarl: 1.9.11
- zipp: 3.19.2
* System:
- OS: Darwin
- architecture:
- 64bit
-
- processor: arm
- python: 3.10.8
- release: 23.6.0
- version: Darwin Kernel Version 23.6.0: Mon Jul 29 21:14:30 PDT 2024; root:xnu-10063.141.2~1/RELEASE_ARM64_T6000
</details>
### More info
_No response_ | open | 2024-09-06T08:40:53Z | 2025-03-21T11:35:19Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20250 | [
"bug",
"needs triage",
"ver: 2.4.x"
] | MalteEbner | 1 |
SYSTRAN/faster-whisper | deep-learning | 432 | When using faster-whisper, how to automatically split sentences | This is my code:
```python
segments, info = model.transcribe(videos_directory_path + "/" + file, beam_size=5, without_timestamps=True)
with open(srt_directory_path + "/" + filename_without_extension + ".csv", "w") as output_file:
for index, segment in enumerate(segments, start=1):
output_file.write("%s\n\n" % segment.text)
print(f"Parse {file} done!-------{index}/{length}")
```
If I want the stored content in segment.text to be a complete sentence, how should I configure the parameters? | closed | 2023-08-20T05:19:07Z | 2023-08-21T10:45:14Z | https://github.com/SYSTRAN/faster-whisper/issues/432 | [] | OlalalalaO | 2 |
hyperspy/hyperspy | data-visualization | 3,382 | Culling Annoying Warnings | I figured I'd make a thread of annoying warnings that people want to "change or adjust" and we can think about better ways of handling them.
I'll start with:
https://github.com/hyperspy/hyperspy/blob/b742845d7f606bc4086f5bec4bc0ca84b8e4104d/hyperspy/signal.py#L5320C1-L5323C18
Which is quite annoying espcially when chaining together multiple map functions or using a distributed backend which will mulitply this message times 100. There also isn't a way to slience it even if you handle the thing that it is warning about. | open | 2024-06-03T13:39:24Z | 2024-06-03T13:39:24Z | https://github.com/hyperspy/hyperspy/issues/3382 | [] | CSSFrancis | 0 |
ultralytics/yolov5 | deep-learning | 13,021 | No module named 'models' | ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
this is code :
import cv2
import math
import torch
import pygame
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from utils.torch_utils import select_device
# Initialize Pygame and Pygame Mixer
pygame.init()
pygame.mixer.init()
Sound = pygame.mixer.Sound(r"C:\Users\ITC\Downloads\mixkit-alert-alarm-1005.wav")
# Initialize YOLOv5
device = select_device('')
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, force_reload=True)
model = attempt_load(r'C:\Users\ITC\Downloads\best.pt', map_location=device)
stride = int(model.stride.max()) # model stride
names = model.module.names if hasattr(model, 'module') else model.names
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
img = frame.copy()
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Inference
img = torch.from_numpy(img).to(device)
img = img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Predict
pred = model(img)[0]
pred = non_max_suppression(pred, 0.5, 0.4)
for i, det in enumerate(pred):
if len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], frame.shape).round()
for *xyxy, conf, cls in reversed(det):
c = int(cls)
confidence = conf.item() * 100
if confidence > 50 and names[c] == 'person': # Change to 'human' if that's your class name
x1, y1, x2, y2 = [int(i) for i in xyxy]
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 5)
cv2.putText(frame, f'{names[c]} {confidence:.2f}%', (x1 + 8, y1 + 100), cv2.FONT_HERSHEY_SIMPLEX,
1, (255, 0, 0), 2)
# Add your Pygame sound logic here
pygame.mixer.Sound.play(Sound)
# You might need to add logic to stop sound when there's no detection
cv2.imshow("command", frame)
if cv2.waitKey(1) == ord('a'):
break
cap.release()
cv2.destroyAllWindows()
and this is the error in juputer
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[7], line 5
3 import torch
4 import pygame
----> 5 from models.experimental import attempt_load
6 from utils.general import non_max_suppression, scale_coords
7 from utils.torch_utils import select_device
ModuleNotFoundError: No module named 'models'
### Additional
_No response_ | closed | 2024-05-17T13:54:55Z | 2024-10-20T19:46:10Z | https://github.com/ultralytics/yolov5/issues/13021 | [
"question",
"Stale"
] | zyad630 | 3 |
ExpDev07/coronavirus-tracker-api | rest-api | 318 | Server error | Hello, I was doing some tests on the page https://coronavirus-tracker-api.herokuapp.com/ but sometimes a service is not available message appears or an application error, I would like to know if it is because they are doing maintenance to the server, also in some endpoints an api failure error message also appears
| closed | 2020-06-18T19:21:12Z | 2020-06-25T12:28:10Z | https://github.com/ExpDev07/coronavirus-tracker-api/issues/318 | [
"bug",
"down"
] | TheApphiver | 0 |
encode/httpx | asyncio | 3,195 | 502 Error When Using Global Proxy | I'm encountering a 502 Bad Gateway error when using httpx to make requests to my local server, but the same requests succeed when using the requests library. Below are the details of the commands I ran and the outputs I received:
>>> import requests
>>> import httpx
>>> httpx.get("http://localhost:6333/collections")
<Response [502 Bad Gateway]>
>>> httpx.get("https://www.google.com")
<Response [200 OK]>
>>> httpx.get("http://httpbin.org/")
<Response [200 OK]>
>>> requests.get("http://localhost:6333/collections")
<Response [200]>
Logs:
I enabled logging to further investigate the issue:
>>> import logging
>>> logging.basicConfig(level=logging.DEBUG)
>>> httpx.get("http://localhost:6333/collections")
DEBUG:httpx:load_ssl_context verify=True cert=None trust_env=True http2=False
DEBUG:httpx:load_verify_locations cafile='C:\\Users\\Lin\\anaconda3\\lib\\site-packages\\certifi\\cacert.pem'
DEBUG:httpx:load_ssl_context verify=True cert=None trust_env=True http2=False
DEBUG:httpx:load_verify_locations cafile='C:\\Users\\Lin\\anaconda3\\lib\\site-packages\\certifi\\cacert.pem'
DEBUG:httpx:load_ssl_context verify=True cert=None trust_env=True http2=False
DEBUG:httpx:load_verify_locations cafile='C:\\Users\\Lin\\anaconda3\\lib\\site-packages\\certifi\\cacert.pem'
DEBUG:httpcore.connection:connect_tcp.started host='127.0.0.1' port=233 local_address=None timeout=5.0 socket_options=None
DEBUG:httpcore.connection:connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x0000020FA7EFE4A0>
DEBUG:httpcore.http11:send_request_headers.started request=<Request [b'GET']>
DEBUG:httpcore.http11:send_request_headers.complete
DEBUG:httpcore.http11:send_request_body.started request=<Request [b'GET']>
DEBUG:httpcore.http11:send_request_body.complete
DEBUG:httpcore.http11:receive_response_headers.started request=<Request [b'GET']>
DEBUG:httpcore.http11:receive_response_headers.complete return_value=(b'HTTP/1.1', 502, b'Bad Gateway', [(b'Connection', b'close'), (b'Content-Length', b'0')])
INFO:httpx:HTTP Request: GET http://localhost:6333/collections "HTTP/1.1 502 Bad Gateway"
DEBUG:httpcore.http11:receive_response_body.started request=<Request [b'GET']>
DEBUG:httpcore.http11:receive_response_body.complete
DEBUG:httpcore.http11:response_closed.started
DEBUG:httpcore.http11:response_closed.complete
I noticed that the connection is to port 233. I'm using the Clash service mode to globally proxy my network traffic, which seems to be causing the 502 error. However, why do other httpx requests, as well as requests library calls, succeed?
>>> httpx.get("https://www.google.com")
<Response [200 OK]>
>>> httpx.get("http://httpbin.org/")
<Response [200 OK]>
>>> requests.get("http://localhost:6333/collections")
<Response [200]>
Thanks~ | closed | 2024-05-11T05:29:10Z | 2024-05-11T08:09:20Z | https://github.com/encode/httpx/issues/3195 | [] | imdoge | 0 |
laurentS/slowapi | fastapi | 106 | Is it possible to bump limitis package version up to 2.1? | https://limits.readthedocs.io/en/stable/storage.html#async-storage
It'll allow us to use async redis | closed | 2022-08-11T11:49:33Z | 2022-11-08T12:07:28Z | https://github.com/laurentS/slowapi/issues/106 | [] | 10ourto | 1 |
zama-ai/concrete-ml | scikit-learn | 550 | how | ## Feature request
A clear and concise description of the feature proposal.
## Motivation
Please outline the motivation for the proposal.
| closed | 2024-03-21T09:07:17Z | 2024-03-21T09:08:51Z | https://github.com/zama-ai/concrete-ml/issues/550 | [] | 1ofvc | 0 |
slackapi/python-slack-sdk | asyncio | 1,319 | chat.postMessage -not_authed? | Hi - I am starting to create an application for my company to send slack messages to specific users via their email. To start, I'm just doing testing around chat.postMessage. However, when I try to run it as per the example on https://api.slack.com/methods/chat.postMessage, I get a not_auth error. I'm still fairly new to APIs, so any help would be much appreciated.
#### The Slack SDK version
slack-sdk==3.19.5
#### Python runtime version
Python 3.9.6pip
#### OS info
ProductName: macOS
ProductVersion: 13.1
BuildVersion: 22C65
Darwin Kernel Version 22.2.0: Fri Nov 11 02:08:47 PST 2022; root:xnu-8792.61.2~4/RELEASE_X86_64
#### Steps to reproduce:
```
import logging
import os
# Import WebClient from Python SDK (github.com/slackapi/python-slack-sdk)
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError
# WebClient instantiates a client that can call API methods
# When using Bolt, you can use either `app.client` or the `client` passed to listeners.
client = WebClient(token=os.environ.get("SLACK_BOT_TOKEN"))
logger = logging.getLogger(__name__)
# ID of the channel you want to send the message to
channel_id = "U04KNHE0LHF"
try:
# Call the chat.postMessage method using the WebClient
result = client.chat_postMessage(
channel=channel_id,
text="Hello world"
)
logger.info(result)
except SlackApiError as e:
logger.error(f"Error posting message: {e}")
```
### Expected result:
The Slack bot sends the message to the user.
### Actual result:
Error posting message: The request to the Slack API failed. (url: https://www.slack.com/api/chat.postMessage)
The server responded with: {'ok': False, 'error': 'not_authed'}
| closed | 2023-01-20T17:32:23Z | 2023-01-20T18:07:37Z | https://github.com/slackapi/python-slack-sdk/issues/1319 | [
"question",
"untriaged"
] | Brian-Wilcove | 4 |
huggingface/transformers | python | 36,911 | Pipeline cannot guess which processor to use with Gemma 3 | ### System Info
Inside a Kaggle kernel:
```
{'platform': 'Linux',
'platform-release': '6.6.56+',
'platform-version': '#1 SMP PREEMPT_DYNAMIC Sun Nov 10 10:07:59 UTC 2024',
'architecture': 'x86_64',
'hostname': 'e3804eb7eb6c',
'ip-address': '172.19.2.2',
'mac-address': '02:42:ac:13:02:02',
'processor': 'x86_64',
'ram': '31 GB'}
```
pip freeze output:
```
absl-py==1.4.0
accelerate==1.2.1
aiofiles==22.1.0
aiohappyeyeballs==2.4.6
aiohttp==3.11.12
aiosignal==1.3.2
aiosqlite==0.21.0
alabaster==1.0.0
albucore==0.0.19
albumentations==1.4.20
alembic==1.14.1
altair==5.5.0
annotated-types==0.7.0
annoy==1.17.3
ansicolors==1.1.8
antlr4-python3-runtime==4.9.3
anyio==3.7.1
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
args==0.1.0
array_record==0.5.1
arrow==1.3.0
arviz==0.20.0
astropy==6.1.7
astropy-iers-data==0.2024.12.16.0.35.48
asttokens==3.0.0
astunparse==1.6.3
async-timeout==5.0.1
atpublic==4.1.0
attrs==25.1.0
audioread==3.0.1
autograd==1.7.0
babel==2.16.0
backcall==0.2.0
bayesian-optimization==2.0.3
beautifulsoup4==4.12.3
betterproto==2.0.0b6
bigframes==1.29.0
bigquery-magics==0.4.0
bitsandbytes==0.45.3
bleach==6.2.0
blinker==1.9.0
blis==0.7.11
blobfile==3.0.0
blosc2==2.7.1
bokeh==3.6.2
Boruta==0.4.3
boto3==1.36.23
botocore==1.36.23
Bottleneck==1.4.2
-e git+https://github.com/SohierDane/BigQuery_Helper@8615a7f6c1663e7f2d48aa2b32c2dbcb600a440f#egg=bq_helper
bqplot==0.12.43
branca==0.8.1
CacheControl==0.14.1
cachetools==5.5.0
Cartopy==0.24.1
catalogue==2.0.10
catboost==1.2.7
category_encoders==2.7.0
certifi==2025.1.31
cesium==0.12.1
cffi==1.17.1
chardet==5.2.0
charset-normalizer==3.4.1
Chessnut==0.4.1
chex==0.1.88
clarabel==0.9.0
click==8.1.7
click-plugins==1.1.1
cligj==0.7.2
clint==0.5.1
cloudpathlib==0.20.0
cloudpickle==3.1.0
cmake==3.31.2
cmdstanpy==1.2.5
colorama==0.4.6
colorcet==3.1.0
colorlog==6.9.0
colorlover==0.3.0
colour==0.1.5
comm==0.2.2
community==1.0.0b1
confection==0.1.5
cons==0.4.6
contourpy==1.3.1
coverage==7.6.12
cryptography==44.0.1
cuda-bindings==12.8.0
cuda-python==12.8.0
cudf-cu12==25.2.0
cufflinks==0.17.3
cuml-cu12==25.2.0
cupy-cuda12x==12.2.0
cuvs-cu12==25.2.0
cvxopt==1.3.2
cvxpy==1.6.0
cycler==0.12.1
cymem==2.0.10
Cython==3.0.11
cytoolz==1.0.1
daal==2025.2.0
dacite==1.9.2
dask==2024.12.1
dask-cuda==25.2.0
dask-cudf-cu12==25.2.0
dask-expr==1.1.21
dataclasses-json==0.6.7
datascience==0.17.6
datasets==3.3.1
datashader==0.17.0
db-dtypes==1.3.1
dbus-python==1.2.18
deap==1.4.2
debugpy==1.8.0
decorator==4.4.2
deepdiff==8.2.0
defusedxml==0.7.1
Deprecated==1.2.15
diffusers==0.31.0
dill==0.3.8
dipy==1.10.0
distributed==2024.12.1
distributed-ucxx-cu12==0.42.0
distro==1.9.0
dlib==19.24.2
dm-tree==0.1.8
dnspython==2.7.0
docker==7.1.0
docker-pycreds==0.4.0
docstring-to-markdown==0.15
docstring_parser==0.16
docutils==0.21.2
dopamine_rl==4.1.0
duckdb==1.1.3
earthengine-api==1.4.3
easydict==1.13
easyocr==1.7.2
editdistance==0.8.1
eerepr==0.0.4
einops==0.8.0
eli5==0.13.0
email_validator==2.2.0
emoji==2.14.1
en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889
entrypoints==0.4
et_xmlfile==2.0.0
etils==1.11.0
etuples==0.3.9
eval_type_backport==0.2.0
exceptiongroup==1.2.2
execnb==0.1.11
Farama-Notifications==0.0.4
fastai==2.7.18
fastcore==1.7.27
fastdownload==0.0.7
fastjsonschema==2.21.1
fastprogress==1.0.3
fastrlock==0.8.2
fasttext==0.9.3
featuretools==1.31.0
filelock==3.17.0
fiona==1.10.1
firebase-admin==6.6.0
Flask==3.1.0
flatbuffers==24.3.25
flax==0.8.5
folium==0.19.2
fonttools==4.55.3
fqdn==1.5.1
frozendict==2.4.6
frozenlist==1.5.0
fsspec==2024.12.0
funcy==2.0
fury==0.12.0
future==1.0.0
fuzzywuzzy==0.18.0
gast==0.6.0
gatspy==0.3
gcsfs==2024.10.0
GDAL==3.6.4
gdown==5.2.0
geemap==0.35.1
gensim==4.3.3
geocoder==1.38.1
geographiclib==2.0
geojson==3.2.0
geopandas==0.14.4
geopy==2.4.1
ghapi==1.0.6
gin-config==0.5.0
gitdb==4.0.11
GitPython==3.1.43
glob2==0.7
google==2.0.3
google-ai-generativelanguage==0.6.10
google-api-core==1.34.1
google-api-python-client==2.155.0
google-auth==2.27.0
google-auth-httplib2==0.2.0
google-auth-oauthlib==1.2.1
google-cloud-aiplatform==1.74.0
google-cloud-automl==1.0.1
google-cloud-bigquery==3.25.0
google-cloud-bigquery-connection==1.17.0
google-cloud-bigtable==2.27.0
google-cloud-core==2.4.1
google-cloud-datastore==2.20.2
google-cloud-firestore==2.19.0
google-cloud-functions==1.19.0
google-cloud-iam==2.17.0
google-cloud-language==2.16.0
google-cloud-pubsub==2.27.1
google-cloud-resource-manager==1.14.0
google-cloud-storage==2.14.0
google-cloud-translate==3.12.1
google-cloud-videointelligence==2.16.0
google-cloud-vision==3.10.0
google-colab @ file:///colabtools/dist/google_colab-1.0.0.tar.gz
google-crc32c==1.6.0
google-genai==0.2.2
google-generativeai==0.8.3
google-pasta==0.2.0
google-resumable-media==2.7.2
googleapis-common-protos==1.66.0
googledrivedownloader==0.4
gpxpy==1.6.2
graphviz==0.20.3
greenlet==3.1.1
grpc-google-iam-v1==0.13.1
grpcio==1.68.1
grpcio-status==1.48.2
grpclib==0.4.8rc2
gspread==6.0.2
gspread-dataframe==3.3.1
gTTS==2.5.4
gym==0.25.2
gym-notices==0.0.8
gymnasium==0.29.0
h11==0.14.0
h2==4.2.0
h2o==3.46.0.6
h5netcdf==1.4.1
h5py==3.12.1
haversine==2.9.0
hep_ml==0.7.3
hf_transfer==0.1.9
holidays==0.63
holoviews==1.20.0
hpack==4.1.0
html5lib==1.1
htmlmin==0.1.12
httpcore==1.0.7
httpimport==1.4.0
httplib2==0.22.0
httpx==0.28.1
huggingface-hub==0.29.0
humanize==4.11.0
hyperframe==6.1.0
hyperopt==0.2.7
ibis-framework==9.2.0
id==1.5.0
idna==3.10
igraph==0.11.8
ImageHash==4.3.1
imageio==2.36.1
imageio-ffmpeg==0.5.1
imagesize==1.4.1
imbalanced-learn==0.12.4
imgaug==0.4.0
immutabledict==4.2.1
importlib-resources==5.13.0
importlib_metadata==8.5.0
imutils==0.5.4
in-toto-attestation==0.9.3
inflect==7.4.0
iniconfig==2.0.0
intel-cmplr-lib-rt==2024.2.0
intel-cmplr-lib-ur==2024.2.0
intel-openmp==2024.2.0
ipyevents==2.0.2
ipyfilechooser==0.6.0
ipykernel==5.5.6
ipyleaflet==0.19.2
ipympl==0.9.6
ipyparallel==8.8.0
ipython==7.34.0
ipython-genutils==0.2.0
ipython-sql==0.5.0
ipytree==0.2.2
ipywidgets==8.1.5
isoduration==20.11.0
isoweek==1.3.3
itsdangerous==2.2.0
Janome==0.5.0
jax==0.4.33
jax-cuda12-pjrt==0.4.33
jax-cuda12-plugin==0.4.33
jaxlib==0.4.33
jedi==0.19.2
jeepney==0.7.1
jellyfish==1.1.0
jieba==0.42.1
Jinja2==3.1.4
jiter==0.8.2
jmespath==1.0.1
joblib==1.4.2
json5==0.10.0
jsonpatch==1.33
jsonpickle==4.0.1
jsonpointer==3.0.0
jsonschema==4.23.0
jsonschema-specifications==2024.10.1
jupyter-console==6.1.0
jupyter-events==0.12.0
jupyter-leaflet==0.19.2
jupyter-lsp==1.5.1
jupyter-ydoc==0.2.5
jupyter_client==8.6.3
jupyter_core==5.7.2
jupyter_server==2.12.5
jupyter_server_fileid==0.9.3
jupyter_server_terminals==0.5.3
jupyter_server_ydoc==0.8.0
jupyterlab==3.6.8
jupyterlab-lsp==3.10.2
jupyterlab_pygments==0.3.0
jupyterlab_server==2.27.3
jupyterlab_widgets==3.0.13
kaggle==1.6.17
kaggle-environments==1.16.11
kagglehub==0.3.9
keras==3.5.0
keras-core==0.1.7
keras-cv==0.9.0
keras-hub==0.18.1
keras-nlp==0.18.1
keras-tuner==1.4.7
keyring==23.5.0
kiwisolver==1.4.7
kornia==0.8.0
kornia_rs==0.1.8
kt-legacy==1.0.5
langchain==0.3.12
langchain-core==0.3.25
langchain-text-splitters==0.3.3
langcodes==3.5.0
langid==1.1.6
langsmith==0.2.3
language_data==1.3.0
launchpadlib==1.10.16
lazr.restfulclient==0.14.4
lazr.uri==1.0.6
lazy_loader==0.4
learntools @ git+https://github.com/Kaggle/learntools@010e3b5035354e15c073a0aca9e202c2e2beb742
leven==1.0.4
libclang==18.1.1
libcudf-cu12==25.2.0
libcuml-cu12==25.2.0
libcuvs-cu12==25.2.0
libkvikio-cu12==25.2.0
libpysal==4.9.2
libraft-cu12==25.2.0
librosa==0.10.2.post1
libucx-cu12==1.18.0
libucxx-cu12==0.42.0
lightgbm @ file:///tmp/lightgbm/lightgbm-4.5.0-py3-none-linux_x86_64.whl
lightning-utilities==0.12.0
lime==0.2.0.1
line_profiler==4.2.0
linkify-it-py==2.0.3
llvmlite==0.43.0
lml==0.1.0
locket==1.0.0
logical-unification==0.4.6
lxml==5.3.0
Mako==1.3.9
mamba==0.11.3
marisa-trie==1.2.1
Markdown==3.7
markdown-it-py==3.0.0
MarkupSafe==3.0.2
marshmallow==3.26.1
matplotlib==3.7.5
matplotlib-inline==0.1.7
matplotlib-venn==1.1.1
mdit-py-plugins==0.4.2
mdurl==0.1.2
miniKanren==1.0.3
missingno==0.5.2
mistune==0.8.4
mizani==0.13.1
mkl==2025.0.1
mkl-fft==1.3.8
mkl-random==1.2.4
mkl-service==2.4.1
mkl-umath==0.1.1
ml-dtypes==0.4.1
mlcrate==0.2.0
mlxtend==0.23.3
mne==1.9.0
model-signing==0.2.0
more-itertools==10.5.0
moviepy==1.0.3
mpld3==0.5.10
mpmath==1.3.0
msgpack==1.1.0
multidict==6.1.0
multimethod==1.12
multipledispatch==1.0.0
multiprocess==0.70.16
multitasking==0.0.11
murmurhash==1.0.11
music21==9.3.0
mypy-extensions==1.0.0
namex==0.0.8
narwhals==1.18.4
natsort==8.4.0
nbclassic==1.1.0
nbclient==0.5.13
nbconvert==6.4.5
nbdev==2.3.34
nbformat==5.10.4
ndindex==1.9.2
nest-asyncio==1.6.0
networkx==3.4.2
nibabel==5.3.2
nilearn==0.10.4
ninja==1.11.1.3
nltk==3.2.4
nose==1.3.7
notebook==6.5.4
notebook_shim==0.2.4
numba==0.60.0
numba-cuda==0.2.0
numexpr==2.10.2
numpy==1.26.4
nvidia-cublas-cu12==12.6.4.1
nvidia-cuda-cupti-cu12==12.6.80
nvidia-cuda-nvcc-cu12==12.6.85
nvidia-cuda-runtime-cu12==12.6.77
nvidia-cudnn-cu12==9.6.0.74
nvidia-cufft-cu12==11.3.0.4
nvidia-curand-cu12==10.3.7.77
nvidia-cusolver-cu12==11.7.1.2
nvidia-cusparse-cu12==12.5.4.2
nvidia-ml-py==12.570.86
nvidia-nccl-cu12==2.23.4
nvidia-nvcomp-cu12==4.1.0.6
nvidia-nvjitlink-cu12==12.6.85
nvtx==0.2.10
nx-cugraph-cu12 @ https://pypi.nvidia.com/nx-cugraph-cu12/nx_cugraph_cu12-24.10.0-py3-none-any.whl
oauth2client==4.1.3
oauthlib==3.2.2
odfpy==1.4.1
olefile==0.47
omegaconf==2.3.0
onnx==1.17.0
openai==1.57.4
opencv-contrib-python==4.10.0.84
opencv-python==4.10.0.84
opencv-python-headless==4.10.0.84
openpyxl==3.1.5
openslide-bin==4.0.0.6
openslide-python==1.4.1
opentelemetry-api==1.29.0
opentelemetry-sdk==1.29.0
opentelemetry-semantic-conventions==0.50b0
opt_einsum==3.4.0
optax==0.2.4
optree==0.13.1
optuna==4.2.1
orbax-checkpoint==0.6.4
orderly-set==5.3.0
orjson==3.10.12
osqp==0.6.7.post3
overrides==7.7.0
packaging==24.2
pandas==2.2.3
pandas-datareader==0.10.0
pandas-gbq==0.25.0
pandas-profiling==3.6.6
pandas-stubs==2.2.2.240909
pandasql==0.7.3
pandocfilters==1.5.1
panel==1.5.4
papermill==2.6.0
param==2.2.0
parso==0.8.4
parsy==2.1
partd==1.4.2
path==17.1.0
path.py==12.5.0
pathlib==1.0.1
pathos==0.3.2
patsy==1.0.1
pdf2image==1.17.0
peewee==3.17.8
peft==0.14.0
pettingzoo==1.24.0
pexpect==4.9.0
phik==0.12.4
pickleshare==0.7.5
pillow==11.0.0
platformdirs==4.3.6
plotly==5.24.1
plotly-express==0.4.1
plotnine==0.14.4
pluggy==1.5.0
ply==3.11
polars==1.9.0
pooch==1.8.2
portpicker==1.5.2
pox==0.3.5
ppft==1.7.6.9
preprocessing==0.1.13
preshed==3.0.9
prettytable==3.12.0
proglog==0.1.10
progressbar2==4.5.0
prometheus_client==0.21.1
promise==2.3
prompt_toolkit==3.0.48
propcache==0.2.1
prophet==1.1.6
proto-plus==1.25.0
protobuf==3.20.3
psutil==5.9.5
psycopg2==2.9.10
ptyprocess==0.7.0
pudb==2024.1.3
py-cpuinfo==9.0.0
py4j==0.10.9.7
pyaml==25.1.0
PyArabic==0.6.15
pyarrow==19.0.1
pyasn1==0.6.1
pyasn1_modules==0.4.1
pybind11==2.13.6
pyclipper==1.3.0.post6
pycocotools==2.0.8
pycparser==2.22
pycryptodome==3.21.0
pycryptodomex==3.21.0
pyct==0.5.0
pycuda==2025.1
pydantic==2.11.0a2
pydantic_core==2.29.0
pydata-google-auth==1.9.0
pydegensac==0.1.2
pydicom==3.0.1
pydot==3.0.3
pydotplus==2.0.2
PyDrive==1.3.1
PyDrive2==1.21.3
pydub==0.25.1
pyemd==1.0.0
pyerfa==2.0.1.5
pyexcel-io==0.6.7
pyexcel-ods==0.6.0
pygame==2.6.1
pygit2==1.16.0
pygltflib==1.16.3
Pygments==2.19.1
PyGObject==3.42.1
PyJWT==2.10.1
pyLDAvis==3.4.1
pylibcudf-cu12==25.2.0
pylibcugraph-cu12==24.10.0
pylibraft-cu12==25.2.0
pymc==5.19.1
pymc3==3.11.4
pymongo==4.11.1
Pympler==1.1
pymystem3==0.2.0
pynvjitlink-cu12==0.4.0
pynvml==12.0.0
pyogrio==0.10.0
Pyomo==6.8.2
PyOpenGL==3.1.7
pyOpenSSL==25.0.0
pyparsing==3.2.0
pypdf==5.3.0
pyperclip==1.9.0
pyproj==3.7.0
pyshp==2.3.1
PySocks==1.7.1
pyspark==3.5.3
pytensor==2.26.4
pytesseract==0.3.13
pytest==8.3.4
python-apt==0.0.0
python-bidi==0.6.6
python-box==7.3.0
python-dateutil==2.9.0.post0
python-json-logger==3.2.1
python-louvain==0.16
python-lsp-jsonrpc==1.1.2
python-lsp-server==1.12.2
python-slugify==8.0.4
python-utils==3.9.1
pytools==2025.1.1
pytorch-ignite==0.5.1
pytorch-lightning==2.5.0.post0
pytz==2025.1
PyUpSet==0.1.1.post7
pyviz_comms==3.0.3
PyWavelets==1.8.0
PyYAML==6.0.2
pyzmq==24.0.1
qdldl==0.1.7.post4
qgrid==1.3.1
qtconsole==5.6.1
QtPy==2.4.3
raft-dask-cu12==25.2.0
rapids-dask-dependency==25.2.0
ratelim==0.1.6
ray==2.42.1
referencing==0.35.1
regex==2024.11.6
requests==2.32.3
requests-oauthlib==1.3.1
requests-toolbelt==1.0.0
requirements-parser==0.9.0
rfc3161-client==0.1.2
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rfc8785==0.1.4
rgf-python==3.12.0
rich==13.9.4
rmm-cu12==25.2.0
rpds-py==0.22.3
rpy2==3.4.2
rsa==4.9
Rtree==1.3.0
s3fs==0.4.2
s3transfer==0.11.2
safetensors==0.4.5
scikit-image==0.25.0
scikit-learn==1.2.2
scikit-learn-intelex==2025.2.0
scikit-multilearn==0.2.0
scikit-optimize==0.10.2
scikit-plot==0.3.7
scikit-surprise==1.1.4
scipy==1.13.1
scooby==0.10.0
scs==3.2.7
seaborn==0.12.2
SecretStorage==3.3.1
securesystemslib==1.2.0
segment_anything @ git+https://github.com/facebookresearch/segment-anything.git@dca509fe793f601edb92606367a655c15ac00fdf
semver==3.0.4
Send2Trash==1.8.3
sentence-transformers==3.3.1
sentencepiece==0.2.0
sentry-sdk==2.19.2
setproctitle==1.3.4
setuptools-scm==8.1.0
shap==0.44.1
shapely==2.0.7
shellingham==1.5.4
Shimmy==1.3.0
sigstore==3.6.1
sigstore-protobuf-specs==0.3.2
sigstore-rekor-types==0.0.18
simple-parsing==0.1.6
SimpleITK==2.4.1
six==1.17.0
sklearn-pandas==2.2.0
slicer==0.0.7
smart-open==7.0.5
smmap==5.0.1
sniffio==1.3.1
snowballstemmer==2.2.0
sortedcontainers==2.4.0
soundfile==0.12.1
soupsieve==2.6
soxr==0.5.0.post1
spacy==3.7.5
spacy-legacy==3.0.12
spacy-loggers==1.0.5
Sphinx==8.1.3
sphinx-rtd-theme==0.2.4
sphinxcontrib-applehelp==2.0.0
sphinxcontrib-devhelp==2.0.0
sphinxcontrib-htmlhelp==2.1.0
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==2.0.0
sphinxcontrib-serializinghtml==2.0.0
SQLAlchemy==2.0.36
sqlglot==25.1.0
sqlparse==0.5.3
squarify==0.4.4
srsly==2.5.0
stable-baselines3==2.1.0
stanio==0.5.1
statsmodels==0.14.4
stopit==1.1.2
StrEnum==0.4.15
stringzilla==3.11.1
stumpy==1.13.0
sympy==1.13.1
tables==3.10.1
tabulate==0.9.0
tbb==2022.0.0
tbb4py==2022.0.0
tblib==3.0.0
tcmlib==1.2.0
tenacity==9.0.0
tensorboard==2.17.1
tensorboard-data-server==0.7.2
tensorflow==2.17.1
tensorflow-cloud==0.1.5
tensorflow-datasets==4.9.7
tensorflow-hub==0.16.1
tensorflow-io==0.37.1
tensorflow-io-gcs-filesystem==0.37.1
tensorflow-metadata==1.13.1
tensorflow-probability==0.24.0
tensorflow-text==2.17.0
tensorflow_decision_forests==1.10.0
tensorstore==0.1.71
termcolor==2.5.0
terminado==0.18.1
testpath==0.6.0
text-unidecode==1.3
textblob==0.17.1
texttable==1.7.0
tf-slim==1.1.0
tf_keras==2.17.0
Theano==1.0.5
Theano-PyMC==1.1.2
thinc==8.2.5
threadpoolctl==3.5.0
tifffile==2024.12.12
tiktoken==0.9.0
timm==1.0.12
tinycss2==1.4.0
tokenizers==0.21.0
toml==0.10.2
tomli==2.2.1
toolz==0.12.1
torch @ https://download.pytorch.org/whl/cu121_full/torch-2.5.1%2Bcu121-cp310-cp310-linux_x86_64.whl
torchaudio @ https://download.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp310-cp310-linux_x86_64.whl
torchinfo==1.8.0
torchmetrics==1.6.1
torchsummary==1.5.1
torchtune==0.5.0
torchvision @ https://download.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp310-cp310-linux_x86_64.whl
tornado==6.3.3
TPOT==0.12.1
tqdm==4.67.1
traitlets==5.7.1
traittypes==0.2.1
transformers @ git+https://github.com/huggingface/transformers@0ebd6651acd32c982fee265b23243b89bdb89577
treelite==4.4.1
trx-python==0.3
tsfresh==0.20.2
tuf==5.1.0
tweepy==4.14.0
typeguard==4.4.1
typer==0.15.1
types-python-dateutil==2.9.0.20241206
types-pytz==2024.2.0.20241003
types-setuptools==75.6.0.20241126
typing-inspect==0.9.0
typing_extensions==4.12.2
tzdata==2025.1
tzlocal==5.2
uc-micro-py==1.0.3
ucx-py-cu12==0.42.0
ucxx-cu12==0.42.0
ujson==5.10.0
umf==0.9.1
update-checker==0.18.0
uri-template==1.3.0
uritemplate==4.1.1
urllib3==2.3.0
urwid==2.6.16
urwid_readline==0.15.1
vega-datasets==0.9.0
visions==0.7.6
vtk==9.3.1
wadllib==1.3.6
Wand==0.6.13
wandb==0.19.1
wasabi==1.1.3
watchdog==6.0.0
wavio==0.0.9
wcwidth==0.2.13
weasel==0.4.1
webcolors==24.11.1
webencodings==0.5.1
websocket-client==1.8.0
websockets==14.1
Werkzeug==3.1.3
widgetsnbextension==4.0.13
woodwork==0.31.0
wordcloud==1.9.4
wrapt==1.17.0
wurlitzer==3.1.1
xarray==2024.11.0
xarray-einstats==0.8.0
xgboost==2.0.3
xlrd==2.0.1
xvfbwrapper==0.2.9
xxhash==3.5.0
xyzservices==2024.9.0
y-py==0.6.2
yarl==1.18.3
ydata-profiling==4.12.2
ydf==0.9.0
yellowbrick==1.5
yfinance==0.2.50
ypy-websocket==0.8.4
zict==3.0.0
zipp==3.21.0
```
### Who can help?
Hi,
it is my first issue here. I hope I do everything right.
I try to run Gemma 3 inside a Kaggle Kernel. When trying to create the pipeline object I get the error `Impossible to guess which processor to use. Please provide a processor instance or a path/identifier to a processor.`
```
def create_pipeline():
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
accelerator = Accelerator()
model_path = "/kaggle/input/gemma-3/transformers/gemma-3-4b-it/1/"
# Load the processor
processor = AutoProcessor.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Load the model with quantization configuration
model = Gemma3ForConditionalGeneration.from_pretrained(
model_path,
device_map="auto",
torch_dtype=torch.bfloat16,
quantization_config=quantization_config,
)
# Create the pipeline with model + tokenizer
pipe = pipeline(
"image-text-to-text",
model=model,
tokenizer=tokenizer, # Pass tokenizer explicitly
)
pipe, model = accelerator.prepare(pipe, model)
return pipe, accelerator
pipe, accelerator = create_pipeline()
```
The full error message:
```
Exception Traceback (most recent call last)
<ipython-input-9-ac024d156de2> in <cell line: 1>()
----> 1 pipe, accelerator = create_pipeline()
<ipython-input-8-124136e0adb8> in create_pipeline()
23
24 # Create the pipeline with model + tokenizer
---> 25 pipe = pipeline(
26 "image-text-to-text",
27 model=model,
/usr/local/lib/python3.10/dist-packages/transformers/pipelines/__init__.py in pipeline(task, model, config, tokenizer, feature_extractor, image_processor, processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)
1134 else:
1135 # Impossible to guess what is the right processor here
-> 1136 raise Exception(
1137 "Impossible to guess which processor to use. "
1138 "Please provide a processor instance or a path/identifier "
Exception: Impossible to guess which processor to use. Please provide a processor instance or a path/identifier to a processor.
```
The Gemma 3 dataset on Kaggle seems to container the tokeniser config file though.
I am not sure if this is a bug in the library or in my code.
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
## Reproducible example
[Here](https://www.kaggle.com/code/thomasmeiner/gemma-3-with-quantisation-debug-notebook-for-hf) is a Kaggle kernel:
* copy & edit
* add Gemma 3 4b-it dataset
* enable T4 x 2 GPU
* run all cells
### Expected behavior
Expected behaviour would be that the code runs with error and recognises the tokeniser. | closed | 2025-03-23T09:48:37Z | 2025-03-23T11:29:53Z | https://github.com/huggingface/transformers/issues/36911 | [
"bug"
] | thomasmeissnercrm | 1 |
FactoryBoy/factory_boy | django | 900 | SubFactory with a None value by default | #### The problem
Occasionally I want to have a SubFactory (or a RelatedFactory) whose value is set to None unless explicitly stated. That is, I would want to `FooFactory().bar` to be None but still be able to use `FooFactory(bar__name='test)` or `FooFactory(bar=BarFactory())`.
```
class BarFactory(DjangoModelFactory):
name = factory.Faker("first_name")
class FooFactory(DjangoModelFactory):
bar = factory.SubFactory(BarFactory)
```
Is this achievable somehow? Obviously I could use `FooFactory(bar=None)` but this is not ideal.
| closed | 2022-01-03T13:57:05Z | 2022-01-04T07:03:35Z | https://github.com/FactoryBoy/factory_boy/issues/900 | [
"Q&A"
] | aleehedl | 2 |
ijl/orjson | numpy | 304 | Support for datetime.timedelta | I tried to serialize dataclass that had a `timedelta` in it. Unfortunately it is not serializable unlike `datetime`objects which kinda comes hand in hand with `datetime`.
```
TypeError: Type is not JSON serializable: datetime.timedelta
``` | closed | 2022-09-27T15:32:54Z | 2022-10-04T19:32:59Z | https://github.com/ijl/orjson/issues/304 | [] | TommyDuc | 1 |
kennethreitz/responder | flask | 326 | Uvicorn 0.5 | Uvicorn 0.5 has been released.
* Auto-reloading will now take effect, without having to use `uvicorn` from the console.
* Multi-worker support is here.
I'd suggest the following:
* Switch `debug=True` to `reload=True` in `uvicorn.run(...)`
* Pin uvicorn to `0.5.*`
* If you want to enable multiworker support (it's not the default *yet*) then use `workers=multiprocessing.cpu_count()`. | closed | 2019-03-04T13:58:26Z | 2024-03-31T00:57:26Z | https://github.com/kennethreitz/responder/issues/326 | [
"bug"
] | tomchristie | 5 |
pytorch/vision | computer-vision | 8,874 | Some v2 transforms silently ignore numpy arrays. | ### 🐛 Describe the bug
```python
import torch
import PIL.Image
import numpy as np
import torchvision as tv
import torchvision.transforms.v2
img_npy = np.zeros((8, 8, 3), dtype=np.uint8)
img_pil = PIL.Image.fromarray(img_npy)
img_tch = torch.zeros((3, 8, 8), dtype=torch.uint8)
def check_resize(tr, img):
try:
img = tr.Resize(64)(img)
return np.array(img).shape
except Exception as ex:
return ex
for tr in [tv.transforms, tv.transforms.v2]:
print(f"{tr.__name__ = }")
print(f"{check_resize(tr, img_npy) = }")
print(f"{check_resize(tr, img_pil) = }")
print(f"{check_resize(tr, img_tch) = }")
print()
```
produces the following output:
```
tr.__name__ = 'torchvision.transforms'
check_resize(tr, img_npy) = TypeError("Unexpected type <class 'numpy.ndarray'>")
check_resize(tr, img_pil) = (64, 64, 3)
check_resize(tr, img_tch) = (3, 64, 64)
tr.__name__ = 'torchvision.transforms.v2'
check_resize(tr, img_npy) = (8, 8, 3)
check_resize(tr, img_pil) = (64, 64, 3)
check_resize(tr, img_tch) = (3, 64, 64)
```
Notice that `check_resize(tr, img_npy)` with `transforms.v2` doesn't actually resize the image.
### Versions
<details>
```
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: Manjaro Linux (x86_64)
GCC version: (GCC) 14.2.1 20240910
Clang version: 18.1.8
CMake version: version 3.31.2
Libc version: glibc-2.40
Python version: 3.12.7 (main, Oct 1 2024, 11:15:50) [GCC 14.2.1 20240910] (64-bit runtime)
Python platform: Linux-5.15.173-1-MANJARO-x86_64-with-glibc2.40
Is CUDA available: True
CUDA runtime version: 12.6.85
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1080 Ti
Nvidia driver version: 550.135
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.9.5.1
/usr/lib/libcudnn_adv.so.9.5.1
/usr/lib/libcudnn_cnn.so.9.5.1
/usr/lib/libcudnn_engines_precompiled.so.9.5.1
/usr/lib/libcudnn_engines_runtime_compiled.so.9.5.1
/usr/lib/libcudnn_graph.so.9.5.1
/usr/lib/libcudnn_heuristic.so.9.5.1
/usr/lib/libcudnn_ops.so.9.5.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 39 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: GenuineIntel
Model name: Intel(R) Core(TM) i7-4770K CPU @ 3.50GHz
CPU family: 6
Model: 60
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 3
CPU(s) scaling MHz: 76%
CPU max MHz: 3900.0000
CPU min MHz: 800.0000
BogoMIPS: 7039.17
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm cpuid_fault invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt dtherm ida arat pln pts md_clear flush_l1d
Virtualization: VT-x
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
L2 cache: 1 MiB (4 instances)
L3 cache: 8 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Unknown: No mitigations
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Mitigation; Microcode
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==2.2.2
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] torch==2.5.1
[pip3] torchvision==0.20.1
[pip3] triton==3.1.0
[conda] Could not collect
```
</details> | closed | 2025-01-22T15:59:04Z | 2025-02-19T16:45:50Z | https://github.com/pytorch/vision/issues/8874 | [] | ruro | 5 |
chaoss/augur | data-visualization | 2,892 | GitLab Messages for Reviews Error | Getting this error in `dev` for GitLab reviews:
```bash
Traceback (most recent call last):
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 451, in trace_task
R = retval = fun(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 734, in __protected_call__
return self.run(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/augur/augur/tasks/gitlab/issues_task.py", line 224, in collect_gitlab_issue_comments
process_gitlab_issue_messages(comments, f"{owner}/{repo}: Gitlab issue messages task", repo_id, logger, session)
File "/home/ubuntu/github/augur/augur/tasks/gitlab/issues_task.py", line 287, in process_gitlab_issue_messages
issues = session.session.query(Issue).filter(Issue.repo_id == repo_id).all()
^^^^^^^^^^^^^^^
AttributeError: 'Session' object has no attribute 'session'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1969, in _exec_single_context
self.dialect.do_execute(
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/default.py", line 922, in do_execute
cursor.execute(statement, parameters)
psycopg2.errors.UndefinedFunction: operator does not exist: character varying = integer[]
LINE 3: WHERE augur_data.repo.repo_git = ARRAY[59,58,57,56,55,54,53,...
^
HINT: No operator matches the given name and argument types. You might need to add explicit type casts.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 468, in trace_task
I, R, state, retval = on_error(task_request, exc, uuid)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 379, in on_error
R = I.handle_error_state(
^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 178, in handle_error_state
return {
^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 231, in handle_failure
task.on_failure(exc, req.id, req.args, req.kwargs, einfo)
File "/home/ubuntu/github/augur/augur/tasks/init/celery_app.py", line 105, in on_failure
self.augur_handle_task_failure(exc, task_id, repo_git, "core_task_failure")
File "/home/ubuntu/github/augur/augur/tasks/init/celery_app.py", line 88, in augur_handle_task_failure
repo = session.query(Repo).filter(Repo.repo_git == repo_git).one()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/query.py", line 2798, in one
return self._iter().one() # type: ignore
^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/query.py", line 2847, in _iter
result: Union[ScalarResult[_T], Result[_T]] = self.session.execute(
^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/session.py", line 2306, in execute
return self._execute_internal(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/session.py", line 2188, in _execute_internal
result: Result[Any] = compile_state_cls.orm_execute_statement(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/context.py", line 293, in orm_execute_statement
result = conn.execute(
^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1416, in execute
return meth(
^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/sql/elements.py", line 516, in _execute_on_connection
return connection._execute_clauseelement(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1639, in _execute_clauseelement
ret = self._execute_context(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1848, in _execute_context
return self._exec_single_context(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1988, in _exec_single_context
self._handle_dbapi_exception(
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 2343, in _handle_dbapi_exception
raise sqlalchemy_exception.with_traceback(exc_info[2]) from e
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1969, in _exec_single_context
self.dialect.do_execute(
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/default.py", line 922, in do_execute
cursor.execute(statement, parameters)
sqlalchemy.exc.ProgrammingError: (psycopg2.errors.UndefinedFunction) operator does not exist: character varying = integer[]
LINE 3: WHERE augur_data.repo.repo_git = ARRAY[59,58,57,56,55,54,53,...
^
HINT: No operator matches the given name and argument types. You might need to add explicit type casts.
[SQL: SELECT augur_data.repo.repo_id AS augur_data_repo_repo_id, augur_data.repo.repo_group_id AS augur_data_repo_repo_group_id, augur_data.repo.repo_git AS augur_data_repo_repo_git, augur_data.repo.repo_path AS augur_data_repo_repo_path, augur_data.repo.repo_name AS augur_data_repo_repo_name, augur_data.repo.repo_added AS augur_data_repo_repo_added, augur_data.repo.repo_type AS augur_data_repo_repo_type, augur_data.repo.url AS augur_data_repo_url, augur_data.repo.owner_id AS augur_data_repo_owner_id, augur_data.repo.description AS augur_data_repo_description, augur_data.repo.primary_language AS augur_data_repo_primary_language, augur_data.repo.created_at AS augur_data_repo_created_at, augur_data.repo.forked_from AS augur_data_repo_forked_from, augur_data.repo.updated_at AS augur_data_repo_updated_at, augur_data.repo.repo_archived_date_collected AS augur_data_repo_repo_archived_date_collected, augur_data.repo.repo_archived AS augur_data_repo_repo_archived, augur_data.repo.tool_source AS augur_data_repo_tool_source, augur_data.repo.tool_version AS augur_data_repo_tool_version, augur_data.repo.data_source AS augur_data_repo_data_source, augur_data.repo.data_collection_date AS augur_data_repo_data_collection_date
FROM augur_data.repo
WHERE augur_data.repo.repo_git = %(repo_git_1)s]
[parameters: {'repo_git_1': [59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]}]
(Background on this error at: https://sqlalche.me/e/20/f405)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1969, in _exec_single_context
self.dialect.do_execute(
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/default.py", line 922, in do_execute
cursor.execute(statement, parameters)
psycopg2.errors.UndefinedFunction: operator does not exist: character varying = integer[]
LINE 3: WHERE augur_data.repo.repo_git = ARRAY[59,58,57,56,55,54,53,...
^
HINT: No operator matches the given name and argument types. You might need to add explicit type casts.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/billiard/pool.py", line 362, in workloop
result = (True, prepare_result(fun(*args, **kwargs)))
^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 649, in fast_trace_task
R, I, T, Rstr = tasks[task].__trace__(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 572, in trace_task
I, _, _, _ = on_error(task_request, exc, uuid)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 379, in on_error
R = I.handle_error_state(
^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 178, in handle_error_state
return {
^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/celery/app/trace.py", line 231, in handle_failure
task.on_failure(exc, req.id, req.args, req.kwargs, einfo)
File "/home/ubuntu/github/augur/augur/tasks/init/celery_app.py", line 105, in on_failure
self.augur_handle_task_failure(exc, task_id, repo_git, "core_task_failure")
File "/home/ubuntu/github/augur/augur/tasks/init/celery_app.py", line 88, in augur_handle_task_failure
repo = session.query(Repo).filter(Repo.repo_git == repo_git).one()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/query.py", line 2798, in one
return self._iter().one() # type: ignore
^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/query.py", line 2847, in _iter
result: Union[ScalarResult[_T], Result[_T]] = self.session.execute(
^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/session.py", line 2306, in execute
return self._execute_internal(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/session.py", line 2188, in _execute_internal
result: Result[Any] = compile_state_cls.orm_execute_statement(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/orm/context.py", line 293, in orm_execute_statement
result = conn.execute(
^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1416, in execute
return meth(
^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/sql/elements.py", line 516, in _execute_on_connection
return connection._execute_clauseelement(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1639, in _execute_clauseelement
ret = self._execute_context(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1848, in _execute_context
return self._exec_single_context(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1988, in _exec_single_context
self._handle_dbapi_exception(
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 2343, in _handle_dbapi_exception
raise sqlalchemy_exception.with_traceback(exc_info[2]) from e
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/base.py", line 1969, in _exec_single_context
self.dialect.do_execute(
File "/home/ubuntu/github/virtualenvs/hosted/lib/python3.11/site-packages/sqlalchemy/engine/default.py", line 922, in do_execute
cursor.execute(statement, parameters)
sqlalchemy.exc.ProgrammingError: (psycopg2.errors.UndefinedFunction) operator does not exist: character varying = integer[]
LINE 3: WHERE augur_data.repo.repo_git = ARRAY[59,58,57,56,55,54,53,...
^
HINT: No operator matches the given name and argument types. You might need to add explicit type casts.
[SQL: SELECT augur_data.repo.repo_id AS augur_data_repo_repo_id, augur_data.repo.repo_group_id AS augur_data_repo_repo_group_id, augur_data.repo.repo_git AS augur_data_repo_repo_git, augur_data.repo.repo_path AS augur_data_repo_repo_path, augur_data.repo.repo_name AS augur_data_repo_repo_name, augur_data.repo.repo_added AS augur_data_repo_repo_added, augur_data.repo.repo_type AS augur_data_repo_repo_type, augur_data.repo.url AS augur_data_repo_url, augur_data.repo.owner_id AS augur_data_repo_owner_id, augur_data.repo.description AS augur_data_repo_description, augur_data.repo.primary_language AS augur_data_repo_primary_language, augur_data.repo.created_at AS augur_data_repo_created_at, augur_data.repo.forked_from AS augur_data_repo_forked_from, augur_data.repo.updated_at AS augur_data_repo_updated_at, augur_data.repo.repo_archived_date_collected AS augur_data_repo_repo_archived_date_collected, augur_data.repo.repo_archived AS augur_data_repo_repo_archived, augur_data.repo.tool_source AS augur_data_repo_tool_source, augur_data.repo.tool_version AS augur_data_repo_tool_version, augur_data.repo.data_source AS augur_data_repo_data_source, augur_data.repo.data_collection_date AS augur_data_repo_data_collection_date
FROM augur_data.repo
WHERE augur_data.repo.repo_git = %(repo_git_1)s]
[parameters: {'repo_git_1': [59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]}]
(Background on this error at: https://sqlalche.me/e/20/f405)
``` | closed | 2024-08-12T21:45:02Z | 2025-03-05T01:49:20Z | https://github.com/chaoss/augur/issues/2892 | [
"bug",
"server"
] | sgoggins | 2 |
tensorpack/tensorpack | tensorflow | 1,148 | python-prctl platform specifier is useless when installing from a wheel | When installing `tensorpack[all]` on Mac `python-prctl` is expected to be skipped using a platform specifier. Unfortunately this works only when installing from a source package, not from a wheel. The reason is that the platform is evaluated in [setup.py](https://github.com/tensorpack/tensorpack/blob/master/setup.py#L60) at the machine which built the wheel, not at the target machine. This affects the `master` at the time of creating this issue.
```
mac$ pip install tensorpack[all]==0.9.4
# [...]
Collecting python-prctl; extra == "all" (from tensorpack[all]==0.9.4)
Using cached https://files.pythonhosted.org/packages/7a/90/61935e2530a76f41e9e4f8ba0fe073d4ad0a3e16c4953156253f939fb057/python-prctl-1.7.tar.gz
Complete output from command python setup.py egg_info:
This module only works on linux
----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /private/var/folders/k6/pp54bs857851lpcsrb51fm1h0000gn/T/pip-install-fda0nlll/python-prctl/
```
This works:
```
mac$ pip install tensorpack[all]==0.9.4 --no-binary tensorpack
```
I'm not sure if the proper platform specifiers work in `install_requires`:
```
# this works well in requirements.txt
python-prctl==1.7; 'linux' in sys_platform
```
| closed | 2019-04-16T10:18:49Z | 2019-04-16T14:19:27Z | https://github.com/tensorpack/tensorpack/issues/1148 | [
"enhancement"
] | bzamecnik | 7 |
microsoft/qlib | machine-learning | 1,691 | Tsne 画图 | help me！
我想知道DDG-DA论文里面的figure2具体是怎么画出来的呀，然后用的是什么数据呢？
```[tasklist]
### Tasks
```
| open | 2023-11-09T12:56:06Z | 2023-11-09T12:56:55Z | https://github.com/microsoft/qlib/issues/1691 | [
"question"
] | lianlin666 | 0 |
youfou/wxpy | api | 218 | 登录之后回调login_callback获取bot相关信息 | login_callback中想获取bot的头像以及昵称信息，
目前似乎只能从qr_callback中获取uuid以及status和二维码。
| open | 2017-10-25T02:58:24Z | 2017-10-26T02:28:29Z | https://github.com/youfou/wxpy/issues/218 | [] | yanpengzhe | 2 |
iperov/DeepFaceLab | machine-learning | 578 | The old Optimizer 2 mode gave the best balance between batch size and speed. Is there any chance we can have it on DFL 2.0? | THIS IS NOT TECH SUPPORT FOR NEWBIE FAKERS
POST ONLY ISSUES RELATED TO BUGS OR CODE
## Expected behavior
*Describe, in some detail, what you are trying to do and what the output is that you expect from the program.*
## Actual behavior
*Describe, in some detail, what the program does instead. Be sure to include any error message or screenshots.*
## Steps to reproduce
*Describe, in some detail, the steps you tried that resulted in the behavior described above.*
## Other relevant information
- **Command lined used (if not specified in steps to reproduce)**: main.py ...
- **Operating system and version:** Windows, macOS, Linux
- **Python version:** 3.5, 3.6.4, ... (if you are not using prebuilt windows binary) | open | 2020-01-26T04:17:20Z | 2023-06-08T20:31:50Z | https://github.com/iperov/DeepFaceLab/issues/578 | [] | pesado1 | 1 |
ultralytics/ultralytics | deep-learning | 19,518 | Metrics all 0 after TensorRT INT8 export for mode val, only INT8 ONNX performs well | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
I succesfully exported my FP32 YOLOv8 OBB (s) model to FP16 and INT8. For FP16 I get nearly the same metrics values like FP32, but the INT8 model performs very bad. My calibration set are 3699 images, I tried with training calibration set (18536 images) too, but the metrics stay all at 0. Different export `batch_sizes=1,8,16` didn't helped.
Update: The problem, must be between the conversion from `ONNX` to `engine` format (see below). There must be a bug between the conversion process, which leads to 0 in all metrics using `engine` model.
Exporter Code:
```python
from ultralytics import YOLO
import argparse
def export_model(model, export_args):
model.export(**export_args)
def main():
parser = argparse.ArgumentParser(description='Export YOLOv8 OBB model to TensorRT with user-configurable parameters.')
parser.add_argument('--model_path', type=str, required=True, help='Path to the trained YOLOv8 model (.pt file).')
parser.add_argument('--export_fp16', type=bool, default=False, help='Export to FP16 TensorRT model.')
parser.add_argument('--export_int8', type=bool, default=False, help='Export to INT8 TensorRT model.')
parser.add_argument('--format', type=str, default='engine', help="Format to export to (e.g., 'engine', 'onnx').")
parser.add_argument('--imgsz', type=int, default=640, help='Desired image size for the model input. Can be an integer for square images or a tuple (height, width) for specific dimensions.')
parser.add_argument('--keras', type=bool, default=False, help='Enables export to Keras format for TensorFlow SavedModel, providing compatibility with TensorFlow serving and APIs.')
parser.add_argument('--optimize', type=bool, default=False, help='Applies optimization for mobile devices when exporting to TorchScript, potentially reducing model size and improving performance.')
parser.add_argument('--half', type=bool, default=False, help='Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware.')
parser.add_argument('--int8', type=bool, default=False, help='Activates INT8 quantization, further compressing the model and speeding up inference with minimal accuracy loss, primarily for edge devices.')
parser.add_argument('--dynamic', type=bool, default=False, help='Allows dynamic input sizes for ONNX, TensorRT and OpenVINO exports, enhancing flexibility in handling varying image dimensions (enforced).')
parser.add_argument('--simplify', type=bool, default=False, help='Simplifies the model graph for ONNX exports with onnxslim, potentially improving performance and compatibility.')
parser.add_argument('--opset', type=int, default=None, help='Specifies the ONNX opset version for compatibility with different ONNX parsers and runtimes. If not set, uses the latest supported version.')
parser.add_argument('--workspace', type=int, default=None, help='Sets the maximum workspace size in GiB for TensorRT optimizations, balancing memory usage and performance; use None for auto-allocation by TensorRT up to device maximum.')
parser.add_argument('--nms', type=bool, default=False, help='Adds Non-Maximum Suppression (NMS) to the exported model when supported (see Export Formats), improving detection post-processing efficiency.')
parser.add_argument('--batch', type=int, default=1, help="Batch size for export. For INT8 it's recommended using a larger batch like batch=8 (calibrated as batch=16))")
parser.add_argument('--device', type=str, default='0', help="Device to use for export (e.g., '0' for GPU 0).")
parser.add_argument('--data', type=str, default=None, help="Path to the dataset configuration file for INT8 calibration.")
args = parser.parse_args()
# Load the final trained YOLOv8 model
model = YOLO(args.model_path, task='obb')
export_args = {
'format': args.format,
'imgsz': args.imgsz,
'keras': args.keras,
'optimize': args.optimize,
'half': args.half,
'int8': args.int8,
'dynamic': args.dynamic,
'simplify': args.simplify,
'opset': args.opset,
'workspace': args.workspace,
'nms': args.nms,
'batch': args.batch,
'device': args.device,
'data': args.data,
}
if args.export_fp16: # data argument isn't needed for FP16 exports since no calibration is required
print('Exporting to FP16 TensorRT model...')
fp16_args = export_args.copy()
fp16_args['half'] = True
fp16_args['int8'] = False
export_model(model, fp16_args)
print('FP16 export completed.')
if args.export_int8: # NOTE: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#enable_int8_c, for INT8 calibration, the kitti_bev.yaml val split with 3769 images is used.
print('Exporting to INT8 TensorRT model...')
int8_args = export_args.copy()
int8_args['half'] = False
int8_args['int8'] = True
export_model(model, int8_args)
print('INT8 export completed.\nThe calibration .cache which can be reused to speed up export of future model weights using the same data, but this may result in poor calibration when the data is vastly different or if the batch value is changed drastically. In these circumstances, the existing .cache should be renamed and moved to a different directory or deleted entirely.')
if not args.export_fp16 and not args.export_int8:
print('No export option selected. Please specify --export_fp16 and/or --export_int8.')
if __name__ == '__main__':
main()
```
Used export command:
```txt
python export_kitti_obb.py --model_path /home/heizung1/ultralytics_yolov8-obb_ob_kitti/ultralytics/kitti_bev_yolo/run_94_Adam_88.8_87.2/weights/best.pt --export_int8 True --int8 True --dynamic=True --batch 1 --data /home/heizung1/ultralytics_yolov8-obb_ob_kitti/ultralytics/cfg/datasets/kitti_bev.yaml
```
Validation script:
```python
from ultralytics import YOLO
model = YOLO('/home/heizung1/ultralytics_yolov8-obb_ob_kitti/ultralytics/kitti_bev_yolo/run_94_Adam_88.8_87.2/weights/best_1.engine', task='obb', verbose=False)
metrics = model.val(data='/home/heizung1/ultralytics_yolov8-obb_ob_kitti/ultralytics/cfg/datasets/kitti_bev.yaml', imgsz=640,
batch=16, save_json=False, save_hybrid=False, conf=0.001, iou=0.5, max_det=300, half=False,
device='0', dnn=False, plots=False, rect=False, split='val', project=None, name=None)
```
Validation output with INT8 TensorRT:
Validation output with INT8 ONNX:
Thank you very much!
### Additional
_No response_ | open | 2025-03-04T17:11:26Z | 2025-03-14T01:33:53Z | https://github.com/ultralytics/ultralytics/issues/19518 | [
"question",
"OBB",
"exports"
] | Petros626 | 19 |
ydataai/ydata-profiling | jupyter | 1,135 | pandas-profiling does not support latest version of matplotlib | ### Missing functionality
Support for latest version of matplotlib, version 3.6.x
### Proposed feature
Update dependencies to support latest version of matplotlib
### Alternatives considered
_No response_
### Additional context
_No response_ | closed | 2022-11-02T15:58:16Z | 2023-08-08T19:27:27Z | https://github.com/ydataai/ydata-profiling/issues/1135 | [
"feature request 💬"
] | tleonhardt | 3 |
blacklanternsecurity/bbot | automation | 1,712 | Convert is_login_page() to excavate YARA rule | closed | 2024-08-27T21:11:29Z | 2024-10-14T02:55:35Z | https://github.com/blacklanternsecurity/bbot/issues/1712 | [
"enhancement",
"low priority"
] | TheTechromancer | 1 |
|
deepfakes/faceswap | deep-learning | 979 | Unable to merge alignment files | ## Expected behavior
Merging two alignment files will generate one file containing alignments from both files.
## Actual behavior
Merge fails every time returning:
`self.final_alignments.file = filename
AttributeError: can't set attribute`
## Steps to reproduce
Running the merge command through the GUI. Selecting two alignment files, and their corresponding faces folder.
## Other relevant information
crash log attached
[crash_report.2020.03.05.124208091265.log](https://github.com/deepfakes/faceswap/files/4294321/crash_report.2020.03.05.124208091265.log)
| closed | 2020-03-05T17:49:49Z | 2020-03-05T17:51:53Z | https://github.com/deepfakes/faceswap/issues/979 | [] | michaeldeprospo | 1 |
autokey/autokey | automation | 588 | Selecting Match phrase case to typed abbreviation also selects its opposite, Ignore case of typed abbreviation | ## Classification:
Bug
## Reproducibility:
Always
AutoKey version:
0.95.10
Both
If the problem is known to be present in more than one version, please list all of those.
Installed via: debs from GitHub
Linux Distribution: kubuntu 18.04 and others
## Summary
Selecting Match phrase case to typed abbreviation also selects its opposite, Ignore case of typed abbreviation in the GUI
## Steps to Reproduce (if applicable)
Define a phrase and select Match phrase case to typed abbreviation
## Expected Results
Just that one option should be selected
## Actual Results
Ignore case of typed abbreviation also becomes automatically selected - which should be mutually exclusive with the selected option
## Notes
In 0.95.10, if you define a phrase and select Match phrase case to typed abbreviation, it automatically also selects Ignore case of typed abbreviation which makes no sense to me. This only works one way. Selecting Ignore case of typed abbreviation does not auto-select Match phrase case to typed abbreviation. I recreated this on both front ends.
I find it most curious that this bug appears in both front ends. I thought most of that code was disjoint.
I did not check which option actually takes effect, but I believe it honors the first option. If it didn't, we would probably have seen numerous error reports starting shortly after 0.95.10 was released (assuming that's where the bug was introduced - which has not been investigated.) | open | 2021-07-29T08:18:16Z | 2023-05-21T18:44:23Z | https://github.com/autokey/autokey/issues/588 | [
"bug",
"autokey-qt",
"autokey-gtk",
"help-wanted",
"user interface",
"easy fix",
"good first issue"
] | josephj11 | 30 |
mljar/mljar-supervised | scikit-learn | 491 | Custom validation/test set - turn off cross-validation (CV) | As title suggests. How do I do it please? | closed | 2021-11-25T11:05:06Z | 2023-05-01T13:34:18Z | https://github.com/mljar/mljar-supervised/issues/491 | [] | cibic89 | 3 |
kizniche/Mycodo | automation | 1,092 | Internal server error after upgrading from version 8.11.0 to 8.12.6 | ### Describe the problem/bug
Mycodo produces an internal server error on version `8.12.6` (after succesfully upgrading from `8.11.0`) with the following log:
```
Fout 500 - Interne Server Fout
Something bad happened but it's probably not your fault. Letting the developers know about these issues is crucial to supporting Mycodo. Please submit a new issue on GitHub with the following diagnostic information and error traceback (copy the entire traceback):
Version: 8.12.6
Database: 6e394f2e8fec
Model: Raspberry Pi 4 Model B Rev 1.4
Release:
Distributor ID: Raspbian
Description: Raspbian GNU/Linux 10 (buster)
Release: 10
Codename: buster
Firmware:
Aug 3 2021 18:14:56
Copyright (c) 2012 Broadcom
version 40787ee5905644f639a2a0f6e00ae12e517a2211 (clean) (release) (start)
Error (Full Traceback):
Traceback (most recent call last):
File "/home/sjoerd/Mycodo/env/lib/python3.7/site-packages/flask/app.py", line 2447, in wsgi_app
response = self.full_dispatch_request()
File "/home/sjoerd/Mycodo/env/lib/python3.7/site-packages/flask/app.py", line 1952, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/home/sjoerd/Mycodo/env/lib/python3.7/site-packages/flask_restx/api.py", line 652, in error_router
return original_handler(e)
File "/home/sjoerd/Mycodo/env/lib/python3.7/site-packages/flask/app.py", line 1821, in handle_user_exception
reraise(exc_type, exc_value, tb)
File "/home/sjoerd/Mycodo/env/lib/python3.7/site-packages/flask/_compat.py", line 39, in reraise
raise value
File "/home/sjoerd/Mycodo/env/lib/python3.7/site-packages/flask/app.py", line 1950, in full_dispatch_request
rv = self.dispatch_request()
File "/home/sjoerd/Mycodo/env/lib/python3.7/site-packages/flask/app.py", line 1936, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File "/home/sjoerd/Mycodo/mycodo/mycodo_flask/routes_general.py", line 86, in index_page
if not flask_login.current_user.index_page:
File "/home/sjoerd/Mycodo/env/lib/python3.7/site-packages/werkzeug/local.py", line 347, in __getattr__
return getattr(self._get_current_object(), name)
AttributeError: 'AnonymousUserMixin' object has no attribute 'index_page'
```
### Versions:
- Database: 6e394f2e8fec
- Model: Raspberry Pi 4 Model B Rev 1.4
Release:
- Distributor ID: Raspbian
- Description: Raspbian GNU/Linux 10 (buster)
- Release: 10
- Codename: buster
Firmware:
- Aug 3 2021 18:14:56
- Copyright (c) 2012 Broadcom
- version 40787ee5905644f639a2a0f6e00ae12e517a2211 (clean) (release) (start)
### Reproducibility
This error occurred after upgrading Mycodo from version `8.11.0` to version ` 8.12.6`. The updating process itself went fine and did not produce any errors.
### Expected behavior
I expect to use the latest version without errors after upgrading.
### Additional context
I was able to get Mycodo working again after restoring. I restored immediatly since my Shiitake are fruiting at the moment so I was not able to grab additional logs. I can reproduce the issue if the logs are desired. (Or perhaps they are still stored somewhere?)
| closed | 2021-09-22T18:26:05Z | 2021-10-28T18:26:00Z | https://github.com/kizniche/Mycodo/issues/1092 | [] | sjoerdschouten | 5 |
darrenburns/posting | rest-api | 174 | Global header setting | Setting headers in a global scope for all the request of a collection.
That would be useful when working with an api that requires authentication.
It will make it much easier to manage the headers passed. | open | 2025-02-01T03:55:28Z | 2025-02-17T17:49:29Z | https://github.com/darrenburns/posting/issues/174 | [] | snikoletopoulos | 2 |
flairNLP/flair | pytorch | 2,886 | Error resuming training of a NER model | I want to resume training a NER model as shown in the tutorials, loading the model checkpoint but running it with :
`trainer.resume(trained_model,
base_path=path + '-resume',
max_epochs=25,
)`
It simply shows me the metrics of the loaded model and does not perform any training. | closed | 2022-08-04T22:50:43Z | 2023-01-07T13:48:20Z | https://github.com/flairNLP/flair/issues/2886 | [
"bug",
"wontfix"
] | fmafelipe | 5 |
flasgger/flasgger | flask | 181 | yaml syntax broken | Hello
I have an issue now that is
https://gist.github.com/anonymous/904c4628fc3f9d23870a915cf0111610
because we have some files like
https://zerobin.net/?e3390f9a981a3ccd#mAvv8zX8ItzuQPs6qxIqXVPDc03d4yspKeQ1mQTeYms=
and our flasgger version is
flasgger==0.8.0
previously
flasgger==0.6.3
It was working fine until I upgraded flasgger version. (maybe I done something else, but I don't mind what)
Any idea on if I should do something or if flasgger should change something ? | closed | 2018-02-19T09:24:56Z | 2018-08-07T14:20:20Z | https://github.com/flasgger/flasgger/issues/181 | [
"bug"
] | eregnier | 7 |
qubvel-org/segmentation_models.pytorch | computer-vision | 842 | Segmentation fault (core dumped) Probelm | 
I have some problem runnung:
trainer.fit(
model,
train_dataloaders=train_dataloader,
val_dataloaders=valid_dataloader,
)
| closed | 2023-12-12T09:26:31Z | 2024-02-18T01:49:34Z | https://github.com/qubvel-org/segmentation_models.pytorch/issues/842 | [
"Stale"
] | sean86428 | 2 |
jupyter-incubator/sparkmagic | jupyter | 56 | Kill all sessions for a given Livy endpoint | When a user adds a new session, the user might find out that leaked/unused Livy sessions are taking resources up and might want to kill some of them.
| closed | 2015-12-04T23:18:49Z | 2015-12-18T22:07:37Z | https://github.com/jupyter-incubator/sparkmagic/issues/56 | [
"kind:enhancement"
] | aggFTW | 3 |
voila-dashboards/voila | jupyter | 772 | Call a python function from an HTML widget | I have created an HTML widget with multiple elements, I want to be able to call a python function from the onclick event of one of them using javascript. If I run the widgets from the jupyter I am able to do something like
```
onclick="IPython.notebook.kernel.execute(`my_function({some_parameter})`)"
```
That way I can programmatically create the HTML elements and make them call a python function with whatever parameter I need, but when running this with Voila I get the error `IPython is not defined`
Is there a way to call a function defined in python from javascript in Voila?
Thank you! | closed | 2020-11-28T10:20:43Z | 2020-12-21T10:41:59Z | https://github.com/voila-dashboards/voila/issues/772 | [] | pabloppp | 5 |
sammchardy/python-binance | api | 1,208 | Spot batch orders | I'm familiar with futures_place_batch_order() on futures, but is there a way to set batch orders on Spot? Can't find any information on it | closed | 2022-06-26T16:05:14Z | 2022-07-01T01:07:39Z | https://github.com/sammchardy/python-binance/issues/1208 | [] | Karlheinzniebuhr | 1 |
netbox-community/netbox | django | 18,198 | Can not create a Duplicate IP-Range with ENFORCE_GLOBAL_UNIQUE set to false | ### Deployment Type
Self-hosted
### Triage priority
N/A
### NetBox Version
V4.1.7
### Python Version
3.12
### Steps to Reproduce
Configuration parameter ENFORCE_GLOBAL_UNIQUE set to false
1. Click on IPAM -> IP Ranges
2. Click on Add
3. Add an IP range (Leave VRF empty)
4. Add the same IP range again (Leave VRF empty)
### Expected Behavior
An overlapping IP range should be created
### Observed Behavior
Error message:
Defined addresses overlap with range xxx.xxx.xxx.xxx-xxx/xx in VRF None
| closed | 2024-12-10T19:12:45Z | 2025-03-12T03:08:47Z | https://github.com/netbox-community/netbox/issues/18198 | [] | antonvdl | 2 |
dynaconf/dynaconf | django | 407 | [bug] Only one CombinedValidator is registered - subsequent are silently ignored | **Describe the bug**
If validators are added through `settings.validators.register()`, only first CombinedValidator is registered - subsequent are silently skipped.
**Analysis**
The root cause is `Validator.__eq__()` method. `ValidatorList.register()` will go through provided validators and add them, but only if they aren't already on the list (`validator not in self`). `in` will return "`True` if an item of *s* is equal to *x*" ([docs](https://docs.python.org/3.8/library/stdtypes.html#common-sequence-operations)). That logic was added in #256 .
When `Validator.__eq__()` compares different objects, it looks into various `Validator` properties and compares them in pair. If they are all the same, `__eq__()` will assume these are two instances of effectively the same validation rule.
The problem is, `CombinedValidator` doesn't have any of these properties, so two completely different `CombinedValidator` will appear to be the same for `__eq__()` method.
**To Reproduce**
In python shell:
```
>>> from dynaconf import Validator
>>> (Validator("foo.foo") | Validator("foo.bar")) == (Validator("bar.foo") & Validator("bar.bar"))
True
```
This should return False, as these two `CombinedValidator`s have nothing in common.
**Environment (please complete the following information):**
- OS: Linux/Fedora32
- Dynaconf master (6c568d687e29ca5ed9806a74f1f4fb7e4b96be2f), 3.0.1
**Additional context**
I might try working on patch, but I'm not sure about best approach. Perhaps we need type comparison inside `__eq__()` (so comparing AndValidator to OrValidator can return early). But how do we compare two `AndValidator`s? Look into combined validators properties recursively? | closed | 2020-09-11T15:50:54Z | 2020-09-16T15:31:31Z | https://github.com/dynaconf/dynaconf/issues/407 | [
"bug"
] | mirekdlugosz | 1 |
AntonOsika/gpt-engineer | python | 311 | how i make it work on fish shell | # Issue Template
╰─λ source venv/bin/activate
venv/bin/activate (line 41): Unsupported use of '='. In fish, please use 'set VIRTUAL_ENV "/home/alex/Desktop/GPT-Plus/gpt-engineer/venv"'.
VIRTUAL_ENV="/home/alex/Desktop/GPT-Plus/gpt-engineer/venv"
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
from sourcing file venv/bin/activate
source: Error while reading file 'venv/bin/activate'
### Steps to Reproduce
1. step 1 open fish shell and do what that readme.md says
2. step 2 when you try to add source venv/bin/activate this env and
3. you get it...
| closed | 2023-06-22T01:49:30Z | 2023-06-29T08:45:34Z | https://github.com/AntonOsika/gpt-engineer/issues/311 | [] | ALEX5402 | 2 |
JaidedAI/EasyOCR | deep-learning | 748 | Using both opencv-python and opencv-python-headless | Hi!
I already have the first package of `opencv-python` and using it for my project. But when i download `EasyOCR` it also download `opencv-python-headless` which cause conflict with the first package:
```bash
cv2.imshow('Original Image', img)
cv2.error: OpenCV(4.5.4) /tmp/pip-req-build-9vck9bv0/opencv/modules/highgui/src/window.cpp:1274: error: (-2:Unspecified error)
The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support.
If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvShowImage'
```
#630 did work for me, so i propose to check it in setup.py if the user has installed `opencv-python<=4.5.4.60`
Thanks! | open | 2022-06-06T14:25:27Z | 2022-06-07T12:36:38Z | https://github.com/JaidedAI/EasyOCR/issues/748 | [] | s39674 | 0 |
babysor/MockingBird | deep-learning | 467 | 奇怪的注意力模型和较低的Loss是什么情况？以及训练vocoder时失败 | **注意力模型居然不是斜的，是直的，而且位于顶部，但是Loss值降低到了0.19。**
**wavrnn不能训练，也没有报错，hifigan显存不够。**
**1k**
一开始的时候没在意。
**3k**
这里有事出去了一下，回来就发现问题了。
**5k**
**10k**
**16k**
**21k**
然后我回来直接终止训练了。工具箱下测试了一下，不是杂音但也不是人话……
我考虑是不是官方自带vocoder的问题，打算自己训练vocoder，但是wavrnn运行直接退出，无报错。batchsize设置4。
hifigan也不能训练，batchsize设置4。
在训练hifigan时还有以下提示
| closed | 2022-03-21T00:31:36Z | 2022-03-29T13:39:05Z | https://github.com/babysor/MockingBird/issues/467 | [] | Okimoto-TK | 2 |
ageitgey/face_recognition | python | 1,327 | Face Detection Accuracy issue | Latest Face_Recognition
* 3.9
* Google Colab
There are few issues with this library which is very well done, I might add. First is that it somehow confuses black faces together, even ones that are clearly distinct. The other is that it sometimes detects weird things as faces.
This is an example of one of those weird detection
| open | 2021-06-15T00:55:27Z | 2021-07-27T13:47:12Z | https://github.com/ageitgey/face_recognition/issues/1327 | [] | SetuBaru | 1 |
alirezamika/autoscraper | automation | 50 | WebScraper | closed | 2021-02-07T17:32:25Z | 2021-02-07T17:33:38Z | https://github.com/alirezamika/autoscraper/issues/50 | [] | jidegade | 0 |
|
albumentations-team/albumentations | deep-learning | 2,426 | [Feature request] Add apply_to_images to RandomShadow | open | 2025-03-11T01:14:40Z | 2025-03-11T01:14:46Z | https://github.com/albumentations-team/albumentations/issues/2426 | [
"enhancement",
"good first issue"
] | ternaus | 0 |
|
kornia/kornia | computer-vision | 2,931 | implement a deterministic two view scene | Add a todo or open a ticket to implement a deterministic two view scene
_Originally posted by @edgarriba in https://github.com/kornia/kornia/pull/2930#discussion_r1640805620_
We have a bunch of tests on the suite that use the random two scenes, but it's not enough since some cases will fail if it isn't deterministic
similar to: https://github.com/kornia/kornia/blob/ca91494a504d04ff23ef5eff0747cee01cb3bcba/testing/geometry/create.py#L34 | open | 2024-06-15T17:35:16Z | 2024-07-02T01:51:11Z | https://github.com/kornia/kornia/issues/2931 | [
"help wanted",
"feature request",
"module: geometry"
] | johnnv1 | 0 |
proplot-dev/proplot | data-visualization | 198 | Show labels of grouped pandas dataframe in one legend | ### Description
Proplot plots the legend for each label instead of listing labels on one legend.
As a result, several legends are stacked together.
### Steps to reproduce
```python
import proplot as plot
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
fig, axs = plot.subplots()
df.groupby('A')['C'].plot(legend=True, ax=axs)
```
**Expected behavior**:
**Actual behavior**:
### Equivalent steps in matplotlib
```python
import matplotlib.pyplot as plt
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
df.groupby('A')['C'].plot(legend=True)
plt.show()
```
### Proplot version
0.6.3
| closed | 2020-06-26T09:46:52Z | 2021-07-03T16:03:56Z | https://github.com/proplot-dev/proplot/issues/198 | [
"integration"
] | zxdawn | 2 |
geex-arts/django-jet | django | 383 | icon-yes, icon-no not showing on themes gray, purple, light green, light blue | It's due to the `$warning-text-color` and `$success-text-color` set to `#fff` on those themes. | open | 2019-01-03T10:10:13Z | 2019-01-05T16:45:17Z | https://github.com/geex-arts/django-jet/issues/383 | [] | aparakian | 0 |
tensorflow/tensor2tensor | deep-learning | 1,710 | Rebulid T2T with single thread | I test the machine translation job with CPU in interactive way through commandway.
It needs about 1 second for decoding one sentence.
I find in this way, mutil threads does not ues at all.
May I rebulid the T2T in single thread way and it may accelerate the decoding speed ? | open | 2019-09-24T06:53:47Z | 2019-09-24T06:53:47Z | https://github.com/tensorflow/tensor2tensor/issues/1710 | [] | Jason-kid | 0 |
fa0311/TwitterInternalAPIDocument | graphql | 274 | Some i18n data is missing. | https://twitter.com/kaonasi_biwa/status/1692736910089941078
@kaonasi-biwa | closed | 2023-08-19T20:28:22Z | 2023-08-21T15:00:57Z | https://github.com/fa0311/TwitterInternalAPIDocument/issues/274 | [] | fa0311 | 0 |
onnx/onnx | machine-learning | 6,162 | The PixelUnshuffle op Cannot be converted to SpaceToDepth | # Ask a Question
### Question
The Pytorch PixelUnshuffle operator is converted as Reshape->Transpose->Reshape.
But what I expect is SpaceToDepth in ONNX.
### Further information
| closed | 2024-06-04T09:45:16Z | 2024-08-24T05:30:20Z | https://github.com/onnx/onnx/issues/6162 | [
"question"
] | iamweiweishi | 3 |
koxudaxi/fastapi-code-generator | fastapi | 207 | Add support for tags | It would be great to have support for tags. I volunteer to implement this, I just don't want to start implementing it before https://github.com/koxudaxi/fastapi-code-generator/pull/203 is merged to avoid merge conflicts.
FastAPI docs: https://fastapi.tiangolo.com/tutorial/path-operation-configuration/#tags | closed | 2021-10-22T13:28:58Z | 2023-01-18T12:28:25Z | https://github.com/koxudaxi/fastapi-code-generator/issues/207 | [] | rominf | 3 |
pytorch/pytorch | deep-learning | 149,586 | UserWarning: Dynamo does not know how to trace the builtin `None.pybind11_object.__new__.` | ### 🐛 Describe the bug
I'm filing an issue since this is a Python built-in (granted the error message implies that it is not since it references PyBind11, but I'm opening an issue anyway since it is caused by using returning/using `None` in a compiled function).
### Versions
2.7.0a0+gitebd087e
cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames @zou3519 @ydwu4 @xmfan @bdhirsh @Chillee @drisspg @yanboliang @BoyuanFeng | open | 2025-03-20T00:32:49Z | 2025-03-21T19:28:30Z | https://github.com/pytorch/pytorch/issues/149586 | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: higher order operators",
"module: compiled autograd",
"module: pt2-dispatcher",
"module: flex attention"
] | cora-codes | 11 |
mithi/hexapod-robot-simulator | dash | 117 | pip install problem (on windows) | requirements.txt requests markupsafe 1.1.1 but werkzeug 2.2.3 requires MarkupSafe 2.1.1 or above | open | 2023-08-01T14:23:21Z | 2023-12-04T11:06:51Z | https://github.com/mithi/hexapod-robot-simulator/issues/117 | [] | bestbinaryboi | 2 |
d2l-ai/d2l-en | pytorch | 2,632 | Equation error in calculus.md | In section [2.4.3 2.4.3. Partial Derivatives and Gradients](https://d2l.ai/chapter_preliminaries/calculus.html#partial-derivatives-and-gradients), the equation seems to be wrong,
<img width="477" alt="Image" src="https://github.com/user-attachments/assets/5fb2a401-164c-4ec2-b657-a368380103d6" />
It should be
<img width="495" alt="Image" src="https://github.com/user-attachments/assets/bd35af03-e7c1-48c4-9fd9-33c2eddd2e3e" />
If so, I would like to create a pr to fix.
Thanks | open | 2025-01-17T14:50:03Z | 2025-01-17T14:50:22Z | https://github.com/d2l-ai/d2l-en/issues/2632 | [] | wsehjk | 0 |
xinntao/Real-ESRGAN | pytorch | 606 | 怎么自定义输出图片的分辨率？ | open | 2023-04-12T12:42:37Z | 2023-04-12T12:42:37Z | https://github.com/xinntao/Real-ESRGAN/issues/606 | [] | TQG1997 | 0 |
|
nvbn/thefuck | python | 1,502 | fish issue on termux with psutil | Error running `eval $(TF_SHELL=fish thefuck --alias)` on fish shell in termux. Also have psutil err when running normally. The fix was to run the eval command using TF_SHELL=fish but that doesn't seem to work here. I'm using https://github.com/DL909/thefuck/ because this repo is just dead and has err due to imp.
Running eval alias command has this error
```
eval $(TF_SHELL=fish thefuck --alias) 21.2s Fri Mar 7 14:16:28 2025
fish: Expected end of the statement, but found a pipe
function fuck -d "Correct your previous console command" set -l fucked_up_command $history[1] env TF_SHELL=fish TF_ALIAS=fuck PYTHONIOENCODING=utf-8 thefuck $fucked_up_command THEFUCK_ARGUMENT_PLACEHOLDER $argv | read -l unfucked_command if [ "$unfucked_command" != "" ] eval $unfucked_command builtin history delete --exact --case-sensitive -- $fucked_up_command builtin history merge end end
^
```
Running regular command has this error
```
fuck 7.6s Fri Mar 7 14:14:36 2025
Traceback (most recent call last):
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/psutil/_pslinux.py", line 1646, in wrapper
return fun(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/psutil/_pslinux.py", line 1890, in create_time
bt = BOOT_TIME or boot_time()
^^^^^^^^^^^
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/psutil/_pslinux.py", line 1561, in boot_time
with open_binary(path) as f:
^^^^^^^^^^^^^^^^^
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/psutil/_common.py", line 766, in open_binary
return open(fname, "rb", buffering=FILE_READ_BUFFER_SIZE)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
PermissionError: [Errno 13] Permission denied: '/proc/stat'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/data/com.termux/files/usr/bin/fuck", line 5, in <module>
from thefuck.entrypoints.not_configured import main
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/thefuck/entrypoints/not_configured.py", line 14, in <module>
from ..shells import shell # noqa: E402
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/thefuck/shells/__init__.py", line 52, in <module>
shell = _get_shell_from_env() or _get_shell_from_proc()
^^^^^^^^^^^^^^^^^^^^^^
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/thefuck/shells/__init__.py", line 45, in _get_shell_from_proc
proc = proc.parent()
^^^^^^^^^^^^^
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/psutil/__init__.py", line 596, in parent
ctime = self.create_time()
^^^^^^^^^^^^^^^^^^
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/psutil/__init__.py", line 772, in create_time
self._create_time = self._proc.create_time()
^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/data/com.termux/files/usr/lib/python3.12/site-packages/psutil/_pslinux.py", line 1648, in wrapper
raise AccessDenied(pid, name) from err
psutil.AccessDenied: (pid=25206, name='fuck')
``` | open | 2025-03-07T14:26:46Z | 2025-03-07T14:26:46Z | https://github.com/nvbn/thefuck/issues/1502 | [] | EnderNon | 0 |
STVIR/pysot | computer-vision | 81 | model size different | I want to train pysot based on mobilenetV2.
The siamrpn_mobilev2_l234_dwxcorr model that downloaded in ModelZoo was 44.9MB in Ubuntu16 system.
But when I train a new model using config in "experiments/siamrpn_mobilev2_l234_dwxcorr/config.yaml", the model size was 75.6MB.
So is this normal？ | closed | 2019-06-28T16:45:21Z | 2019-06-29T00:43:09Z | https://github.com/STVIR/pysot/issues/81 | [] | MaxLin86 | 1 |
aminalaee/sqladmin | fastapi | 714 | Support the Aiohttp framework | ### Checklist
- [X] There are no similar issues or pull requests for this yet.
### Is your feature related to a problem? Please describe.
Hi there,
I think this project is really cool and it can use some [aiohttp](https://docs.aiohttp.org/en/stable/) support.
### Describe the solution you would like.
_No response_
### Describe alternatives you considered
_No response_
### Additional context
_No response_ | closed | 2024-02-19T00:41:09Z | 2024-03-14T10:22:26Z | https://github.com/aminalaee/sqladmin/issues/714 | [] | anorprogrammer | 2 |
robotframework/robotframework | automation | 5,376 | Process: Kill process if Robot's timeout occurs when waiting for process to end | Issue #5345 reported that Robot's timeouts weren't able to stop `Run Process` or `Wait For Process` keywords. That was fixed so that these keywords can be stopped, but processes that keywords were waiting for were left running. Leaving process on background especially is likely not a good idea, especially because they often have hung in this case. This issue proposes killing the processes instead.
Killing processes if Robot's timeout occur requires handling the timeout in the library code. That is actually surprisingly easy by catching `robot.errors.TimeoutError` and re-raising it once the process has been killed. There could be other libraries that want to do such cleanup as well, and documenting how to do that in the User Guide is probably a good idea. I'll submit a separate issue about that.
Notice that killing process as proposed above doesn't fully prevent processes to be left running. That can still happen if you use `Start Process` and Robot's timeout occurs before `Wait For Process` is called. We could enhance the library by adding some kind of auto-closing functionality to it, but I don't consider that too high priority because the library already has `Terminate All Processes` that can be used in test or suite teardown. Such an enhancement should anyway get its own issue. | closed | 2025-03-21T09:32:26Z | 2025-03-21T09:42:11Z | https://github.com/robotframework/robotframework/issues/5376 | [
"priority: medium",
"effort: small"
] | pekkaklarck | 0 |
cvat-ai/cvat | computer-vision | 8,213 | LambdaFunction does not map attrs of skeleton sublabels | ### Actions before raising this issue
- [X] I searched the existing issues and did not find anything similar.
- [X] I read/searched [the docs](https://docs.cvat.ai/docs/)
### Steps to Reproduce
1. Create skeleton
2. Add confidence attr to individual points
3. Have nuclio function return a skeleton with keypoints that have confidence sublabels
4. CVAT ui, the confidence labels will be unchanged / have the default values.
### Expected Behavior
nuclio functions should be able to return skeletons with sublabels that have attrbibutes.
### Possible Solution
Update views.py:
https://github.com/josiahls/cvat/blob/patch-1/cvat/apps/lambda_manager/views.py
```python
def update_mapping(_mapping, _model_labels, _db_labels):
logger.debug("Starting update_mapping with _mapping: %s, _model_labels: %s, _db_labels: %s", _mapping, _model_labels, _db_labels)
copy = deepcopy(_mapping)
for model_label_name, mapping_item in copy.items():
try:
logger.debug("Processing model_label_name: %s", model_label_name)
md_label = next(filter(lambda x: x['name'] == model_label_name, _model_labels))
db_label = next(filter(lambda x: x.name == mapping_item['name'], _db_labels))
mapping_item.setdefault('attributes', {})
mapping_item['md_label'] = md_label
mapping_item['db_label'] = db_label
logger.debug("Mapped md_label: %s to db_label: %s", md_label, db_label)
if md_label['type'] == 'skeleton' and db_label.type == 'skeleton':
mapping_item['sublabels'] = update_mapping(
mapping_item['sublabels'],
md_label['sublabels'],
db_label.sublabels.all()
)
logger.debug("Updated sublabels for label: %s", model_label_name)
# Ensure sublabel attributes are also mapped
for sub_md_label in md_label['sublabels']:
sub_md_name = sub_md_label['name']
sub_db_label = next(filter(lambda x: x.name == sub_md_name, db_label.sublabels.all()), None)
if sub_db_label:
sublabel_attr_mapping = {
attr['name']: attr['name'] for attr in sub_md_label['attributes']
}
mapping_item['sublabels'][sub_md_name]['attributes'] = sublabel_attr_mapping
logger.debug("Mapped sublabel attributes for sublabel: %s - %s", sub_md_name, sublabel_attr_mapping)
except Exception as e:
logger.error("Error processing label: %s, Error: %s", model_label_name, e)
logger.debug("Finished update_mapping with result: %s", copy)
return copy
```
### Context
nulcio autolabelling for skeletons that have keypoints with attributes.
### Environment
```Markdown
git log -2
commit c99b4503b3d6b2f04413cc8d5dd666bab1e40ece (HEAD -> patch-1, origin/patch-1)
Merge: d931645b1 ab636fb14
Author: josiahls <josiahls@users.noreply.github.com>
Date: Fri May 31 14:59:42 2024 -0400
Merge branch 'cvat-ai:develop' into patch-1
commit ab636fb1455a49bb820ee697c394b9dc82d66830 (origin/develop, origin/HEAD)
Author: Boris Sekachev <boris.sekachev@yandex.ru>
Date: Fri May 31 10:38:44 2024 +0300
Squashed `zoom:image` and `send:exception` client events (#7953)
```
| closed | 2024-07-23T20:00:51Z | 2024-08-08T08:39:14Z | https://github.com/cvat-ai/cvat/issues/8213 | [
"bug"
] | josiahls | 1 |
InstaPy/InstaPy | automation | 6,579 | TypeError: 'module' object is not callable | py bot.py
Traceback (most recent call last):
File "C:\Users\****\OneDrive\Desktop\bot.py", line 3, in <module>
session = instapy (username="****", password="********")
TypeError: 'module' object is not callable
not sure what is not working help wanted | open | 2022-04-10T01:50:58Z | 2022-04-10T01:50:58Z | https://github.com/InstaPy/InstaPy/issues/6579 | [] | rzrv | 0 |
autokey/autokey | automation | 834 | Review and update pip-requirements.txt | ### AutoKey is a Xorg application and will not function in a Wayland session. Do you use Xorg (X11) or Wayland?
Xorg
### Has this issue already been reported?
- [X] I have searched through the existing issues.
### Is this a question rather than an issue?
- [X] This is not a question.
### What type of issue is this?
Enhancement
### Choose one or more terms that describe this issue:
- [ ] autokey triggers
- [ ] autokey-gtk
- [ ] autokey-qt
- [ ] beta
- [ ] bug
- [ ] critical
- [X] development
- [ ] documentation
- [ ] enhancement
- [ ] installation/configuration
- [ ] phrase expansion
- [ ] scripting
- [X] technical debt
- [ ] user interface
### Other terms that describe this issue if not provided above:
_No response_
### Which Linux distribution did you use?
_No response_
### Which AutoKey GUI did you use?
None
### Which AutoKey version did you use?
_No response_
### How did you install AutoKey?
_No response_
### Can you briefly describe the issue?
The contents of the [pip-requirements.txt](https://github.com/autokey/autokey/blob/master/pip-requirements.txt) needs to be reviewed and updated.
### Can the issue be reproduced?
Always
### What are the steps to reproduce the issue?
1. Examine the [pip-requirements.txt](https://github.com/autokey/autokey/blob/beta/pip-requirements.txt) file on the **beta** branch.
2. Examine the [pip-requirements.txt](https://github.com/autokey/autokey/blob/develop/pip-requirements.txt) file on the **develop** branch.
3. Examine the [pip-requirements.txt](https://github.com/autokey/autokey/blob/master/pip-requirements.txt) file on the **master** branch.
### What should have happened?
The contents should be current.
### What actually happened?
It's possible the contents are outdated.
### Do you have screenshots?
_No response_
### Can you provide the output of the AutoKey command?
_No response_
### Anything else?
The [PyPI](https://pypi.org) page may be useful for searching for each of the libraries/modules listed in the **pip-requirements.txt** file on each branch to find out if any libraries/modules need to be added or removed. | open | 2023-04-06T20:40:59Z | 2023-05-06T17:22:58Z | https://github.com/autokey/autokey/issues/834 | [
"installation/configuration"
] | Elliria | 2 |
CTFd/CTFd | flask | 2,193 | CTFd Function Question. | hello sir. I would like to ask a second question.
---
To run CTFd, I'm trying to run it with docker on a VM with 8 cores and 16RAM.
I'd like to modify the options to improve CTFd performance, but the guide doesn't seem to exist.
Can you give me a guide to improving the performance?
If I'm wrong, can you give me a guide to improving the performance?
---
I'm using dockerchallenge mode with the ultimate library for CTFd. (this, https://github.com/andyjsmith/CTFd-Docker-Plugin)
It works very well, but I have one problem when displaying docker info in CTFd.
Docker information is exposed via elements of `<span class='connection-info`> within the CTFd template.
(this. core/templates/challenge.html)
<img width="482" alt="스크린샷 2022-09-29 오전 10 24 20" src="https://user-images.githubusercontent.com/50125695/192917478-90add1b6-1965-4e67-8567-7c66a301e2f2.png">
But if you use this element, you have to hit the `Get Connection info` button every time you open a challenge,
It doesn't seem to work dynamically. Is there any solution for this sir?
The way I think is to click the `Get Connection info' button only once for the first time when opening the docker challenge, and the connection information should be displayed instead of this button while the docker container is open.
| closed | 2022-09-29T01:28:11Z | 2022-10-09T00:26:31Z | https://github.com/CTFd/CTFd/issues/2193 | [] | dhje0ng | 0 |
mlflow/mlflow | machine-learning | 14,807 | [BUG] Description editor doesnt support dark mode | ### MLflow version
2.20.4.dev0
### System information
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: mac
- **Python version**: 3.9
- **yarn version, if running the dev UI**: 1.22
### Describe the problem
<img width="660" alt="Image" src="https://github.com/user-attachments/assets/384e2c05-a989-451b-89a1-ad2937407a3f" />
- Description editor doesnt support dark mode
### Steps to reproduce the bug
- Turn on Dark mode
- open to edit description
- the editor view doesnt support dark mode
### Code to generate data required to reproduce the bug
_No response_
### Is the console panel in DevTools showing errors relevant to the bug?
_No response_
### Does the network panel in DevTools contain failed requests relevant to the bug?
_No response_ | closed | 2025-03-03T10:56:24Z | 2025-03-04T06:49:00Z | https://github.com/mlflow/mlflow/issues/14807 | [
"bug",
"area/uiux"
] | Gumichocopengin8 | 2 |
pydantic/logfire | pydantic | 651 | logfire with distributed package like CLI | ### Question
Hello, I would like to use logfire to collect logs from a distributed pip package that acts as a CLI.
The issue is that for now the only easy method I see to log into my logfire project would be to share the write_token to everyone through the pip package. Is there a way you would recommend to go differently about that ?
An authentication system could be put in place but then I would need to be able to create write_token programmatically for each new user, is it something you facilitate ? Or do you have other suggestions? | closed | 2024-12-06T08:02:24Z | 2024-12-16T13:51:07Z | https://github.com/pydantic/logfire/issues/651 | [
"Question"
] | grll | 2 |
pandas-dev/pandas | pandas | 60,343 | BUG (string): contruction of Series / Index fails from dict keys when "str" dtype is specified explicitly | When not specifying a dtype (inferring the type), construction of `Index` or `Series` from dict keys goes fine:
```python
>>> pd.options.future.infer_string = True
>>> d = {"a": 1, "b": 2}
>>> pd.Index(d.keys())
Index(['a', 'b'], dtype='str')
```
But if you explicitly specify the dtype, then it fails:
```python
>>> pd.Index(d.keys(), dtype="str")
...
File ~/scipy/repos/pandas/pandas/core/arrays/string_arrow.py:206, in ArrowStringArray._from_sequence(cls, scalars, dtype, copy)
203 return cls(pc.cast(scalars, pa.large_string()))
205 # convert non-na-likes to str
--> 206 result = lib.ensure_string_array(scalars, copy=copy)
207 return cls(pa.array(result, type=pa.large_string(), from_pandas=True))
File lib.pyx:727, in pandas._libs.lib.ensure_string_array()
File lib.pyx:822, in pandas._libs.lib.ensure_string_array()
ValueError: Buffer has wrong number of dimensions (expected 1, got 0)
```
The reason is that at that point we pass the data directly to the dtype's array `_from_sequence` instead of first pre-processing the data into a numpy array, and `_from_sequence` calling `ensure_string_array` directly doesn't seem to be able to handle dict keys (although we do call `np.asarray(..)` inside `ensure_string_array`, so not entirely sure what is going wrong)
| closed | 2024-11-17T08:31:05Z | 2025-01-26T11:29:27Z | https://github.com/pandas-dev/pandas/issues/60343 | [
"Bug",
"Strings",
"Constructors"
] | jorisvandenbossche | 9 |
ymcui/Chinese-BERT-wwm | nlp | 76 | 对RoBERTa-wwm-ext-large模型的疑问 | 您好！在使用RoBERTa-wwm-ext-large模型的时候，我发现似乎缺少了MLM层的参数（预测句子中某个字几乎是乱的）。
请问确实是缺少了这层参数吗？能否发布添加了这层参数的RoBERTa-wwm-ext-large模型呢？ | closed | 2019-11-19T08:03:26Z | 2020-12-29T08:11:56Z | https://github.com/ymcui/Chinese-BERT-wwm/issues/76 | [] | AnShengqiang | 5 |
davidteather/TikTok-Api | api | 360 | ERROR: No matching distribution found for playwright (from TikTokApi) | **Describe the error**
My English is not good, you can see blow.
**The buggy code**
pip install TikTokApi --upgrade
**Error Trace (if any)**
```
Collecting TikTokApi
Using cached TikTokApi-3.7.9.tar.gz (55 kB)
Requirement already satisfied, skipping upgrade: requests in ./anaconda3/envs/aispider/lib/python3.6/site-packages (from TikTokApi) (2.23.0)
ERROR: Could not find a version that satisfies the requirement playwright (from TikTokApi) (from versions: none)
ERROR: No matching distribution found for playwright (from TikTokApi)
```
**Desktop (please complete the following information):**
- OS: CentOS Linux release 7.4.1708 (Core)
- TikTokApi Version 3.7.9
**Additional context**
try1: pip install playwright -- upgrade
```
ERROR: Could not find a version that satisfies the requirement playwright (from versions: none)
ERROR: No matching distribution found for playwright
```
try2: npm i -D playwright
```
> playwright@1.6.1 install /search/odin/meng/node_modules/playwright
> node install.js
(node:121287) UnhandledPromiseRejectionWarning: Error: EACCES: permission denied, mkdir '/search/odin/meng/.cache/ms-playwright'
(Use `node --trace-warnings ...` to show where the warning was created)
(node:121287) UnhandledPromiseRejectionWarning: Unhandled promise rejection. This error originated either by throwing inside of an async function without a catch block, or by rejecting a promise which was not handled with .catch(). To terminate the node process on unhandled promise rejection, use the CLI flag `--unhandled-rejections=strict` (see https://nodejs.org/api/cli.html#cli_unhandled_rejections_mode). (rejection id: 1)
(node:121287) [DEP0018] DeprecationWarning: Unhandled promise rejections are deprecated. In the future, promise rejections that are not handled will terminate the Node.js process with a non-zero exit code.
npm WARN saveError ENOENT: no such file or directory, open '/search/odin/meng/package.json'
npm notice created a lockfile as package-lock.json. You should commit this file.
npm WARN enoent ENOENT: no such file or directory, open '/search/odin/meng/package.json'
npm WARN ws@7.4.0 requires a peer of bufferutil@^4.0.1 but none is installed. You must install peer dependencies yourself.
npm WARN ws@7.4.0 requires a peer of utf-8-validate@^5.0.2 but none is installed. You must install peer dependencies yourself.
npm WARN meng No description
npm WARN meng No repository field.
npm WARN meng No README data
npm WARN meng No license field.
+ playwright@1.6.1
added 37 packages from 83 contributors in 3.811s
3 packages are looking for funding
run `npm fund` for details
```
try 3: pip install TikTokApi-pyppeteer
I write
```
from TikTokApi import TikTokApi
api = TikTokApi(debug=True)
results = 10
trending = api.trending(count=results)
for tiktok in trending:
# Prints the id of the tiktok
print(tiktok['id'])
print(len(trending))
```
to test.py. when I python test.py, I got this code:
```
Class initialized
[W:pyppeteer.chromium_downloader] start chromium download.
Download may take a few minutes.
The following error occurred, but it was ignored.
'browser' object has no attribute 'timezone_name'
[W:pyppeteer.chromium_downloader] start chromium download.
Download may take a few minutes.
Traceback (most recent call last):
File "test.py", line 6, in <module>
trending = api.trending(count=results)
File "/search/odin/meng/anaconda3/envs/aispider/lib/python3.6/site-packages/TikTokApi/tiktok.py", line 222, in trending
res = self.getData(b, **kwargs)
File "/search/odin/meng/anaconda3/envs/aispider/lib/python3.6/site-packages/TikTokApi/tiktok.py", line 107, in getData
query = {"verifyFp": b.verifyFp, "did": b.did, "_signature": b.signature}
AttributeError: 'browser' object has no attribute 'signature'
``` | closed | 2020-11-16T08:11:36Z | 2020-11-17T05:42:50Z | https://github.com/davidteather/TikTok-Api/issues/360 | [
"installation_help"
] | mengguiyouziyi | 8 |
holoviz/panel | jupyter | 7,333 | Tabulator selection with `pd.MultiIndex` is not working in Panel 1.5.1 | Worked in Panel 1.5, Without doing a `git bisect` likely it is https://github.com/holoviz/panel/pull/7304
Two examples:
``` python
import panel as pn
import pandas as pd
pn.extension("tabulator")
index = pd.MultiIndex.from_tuples([(i, j) for i in range(10) for j in range(10)], names=["A", "B"])
df = pd.DataFrame(index=index, data={"C": range(100)})
w = pn.widgets.Tabulator(df, pagination="remote")
w.on_click(lambda x: print(x))
w.servable()
```
<details>
<summary>Traceback </summary>
``` python-traceback
message: Message 'PATCH-DOC' content: {'events': [{'kind': 'MessageSent', 'msg_type': 'bokeh_event', 'msg_data': {'type': 'event', 'name': 'cell-click', 'values': {'type': 'map', 'entries': [['model', {'id': 'p1220'}], ['column', 'B'], ['row', 9]]}}}]}
error: ValueError('The Tabulator widget expects the provided `value` Pandas DataFrame to have unique indexes, in particular when it has to deal with click or edit events. Found this duplicate index: 9')
Traceback (most recent call last):
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/server/protocol_handler.py", line 94, in handle
work = await handler(message, connection)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/server/session.py", line 94, in _needs_document_lock_wrapper
result = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/server/session.py", line 286, in _handle_patch
message.apply_to_document(self.document, self)
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/protocol/messages/patch_doc.py", line 104, in apply_to_document
invoke_with_curdoc(doc, lambda: doc.apply_json_patch(self.payload, setter=setter))
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/document/callbacks.py", line 453, in invoke_with_curdoc
return f()
^^^
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/protocol/messages/patch_doc.py", line 104, in <lambda>
invoke_with_curdoc(doc, lambda: doc.apply_json_patch(self.payload, setter=setter))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/document/document.py", line 391, in apply_json_patch
DocumentPatchedEvent.handle_event(self, event, setter)
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/document/events.py", line 244, in handle_event
event_cls._handle_event(doc, event)
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/document/events.py", line 279, in _handle_event
cb(event.msg_data)
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/document/callbacks.py", line 400, in trigger_event
model._trigger_event(event)
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/util/callback_manager.py", line 111, in _trigger_event
self.document.callbacks.notify_event(cast(Model, self), event, invoke)
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/document/callbacks.py", line 262, in notify_event
invoke_with_curdoc(doc, callback_invoker)
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/document/callbacks.py", line 453, in invoke_with_curdoc
return f()
^^^
File "/home/shh/miniconda3/envs/holoviz/lib/python3.12/site-packages/bokeh/util/callback_manager.py", line 107, in invoke
cast(EventCallbackWithEvent, callback)(event)
File "/home/shh/projects/holoviz/repos/panel/panel/reactive.py", line 572, in _server_event
self._comm_event(doc, event)
File "/home/shh/projects/holoviz/repos/panel/panel/reactive.py", line 559, in _comm_event
state._handle_exception(e)
File "/home/shh/projects/holoviz/repos/panel/panel/io/state.py", line 468, in _handle_exception
raise exception
File "/home/shh/projects/holoviz/repos/panel/panel/reactive.py", line 557, in _comm_event
self._process_bokeh_event(doc, event)
File "/home/shh/projects/holoviz/repos/panel/panel/reactive.py", line 494, in _process_bokeh_event
self._process_event(event)
File "/home/shh/projects/holoviz/repos/panel/panel/widgets/tables.py", line 1343, in _process_event
self._validate_iloc(idx, iloc)
File "/home/shh/projects/holoviz/repos/panel/panel/widgets/tables.py", line 1300, in _validate_iloc
raise ValueError(
ValueError: The Tabulator widget expects the provided `value` Pandas DataFrame to have unique indexes, in particular when it has to deal with click or edit events. Found this duplicate index: 9
```
</details>
``` python
import panel as pn
import pandas as pd
pn.extension("tabulator")
index = pd.MultiIndex.from_tuples([(i, j) for i in range(10) for j in range(10)], names=["A", "B"])
df = pd.DataFrame(index=index, data={"C": range(100)})
w = pn.widgets.Tabulator(df, pagination="remote", selectable='checkbox')
w.servable()
b = pn.widgets.Button(name='Print selected rows', on_click=lambda x: print(w.selection))
b.servable()
```
Will just return an empty list | closed | 2024-09-27T15:13:19Z | 2024-09-30T10:34:50Z | https://github.com/holoviz/panel/issues/7333 | [
"component: tabulator"
] | hoxbro | 0 |
ivy-llc/ivy | tensorflow | 27,991 | Fix Frontend Failing Test: paddle - tensor.paddle.Tensor.any | To-do List: https://github.com/unifyai/ivy/issues/27500 | closed | 2024-01-22T16:50:43Z | 2024-01-23T15:29:19Z | https://github.com/ivy-llc/ivy/issues/27991 | [
"Sub Task"
] | Sai-Suraj-27 | 0 |
pytest-dev/pytest-xdist | pytest | 230 | loadscope and flake8 don't work together with one node | I'm not sure if this is a loadscope bug or a problem with the implementation of `pytest-flake8` but when I try to run `--flake8 --dist=loadscope -n 1` everything hangs:
```
$ pytest -v --dist=loadscope -n 1 --flake8 --fulltrace tests/test_register.py
============================= test session starts ==============================
platform darwin -- Python 3.6.0, pytest-3.2.0, py-1.4.34, pluggy-0.4.0 -- /Users/timj/work/lsstsw3/miniconda/bin/python
cachedir: .cache
rootdir: /Volumes/G-RAID with Thunderbolt/transient/lsstsw3/build/pipe_tasks, inifile: setup.cfg
plugins: session2file-0.1.9, forked-0.3.dev0+g1dd93f6.d20170815, xdist-1.19.2.dev0+g459d52e.d20170815, flake8-0.8.1
[gw0] darwin Python 3.6.0 cwd: /Volumes/G-RAID with Thunderbolt/transient/lsstsw3/build/pipe_tasks
[gw0] Python 3.6.0 |Continuum Analytics, Inc.| (default, Dec 23 2016, 13:19:00) -- [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]
gw0 [5]
scheduling tests via LoadScopeScheduling
^C
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! KeyboardInterrupt !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
```
The following commands all work fine:
```
$ pytest -v --dist=loadscope -n 1 --fulltrace tests/test_register.py
$ pytest -v --dist=loadscope -n 2 --flake8 --fulltrace tests/test_register.py
$ pytest -v -n 1 --flake8 --fulltrace tests/test_register.py
$ pytest -v -n 2 --flake8 --fulltrace tests/test_register.py
```
leading to the conclusion that everything hangs only when one subprocess is used and loadscope is enabled and flake8 testing is enabled.
```
$ pytest -v --dist=loadscope -n 2 --flake8 --fulltrace tests/test_register.py
============================= test session starts ==============================
platform darwin -- Python 3.6.0, pytest-3.2.0, py-1.4.34, pluggy-0.4.0 -- /Users/timj/work/lsstsw3/miniconda/bin/python
cachedir: .cache
rootdir: /Volumes/G-RAID with Thunderbolt/transient/lsstsw3/build/pipe_tasks, inifile: setup.cfg
plugins: session2file-0.1.9, forked-0.3.dev0+g1dd93f6.d20170815, xdist-1.19.2.dev0+g459d52e.d20170815, flake8-0.8.1
[gw0] darwin Python 3.6.0 cwd: /Volumes/G-RAID with Thunderbolt/transient/lsstsw3/build/pipe_tasks
[gw1] darwin Python 3.6.0 cwd: /Volumes/G-RAID with Thunderbolt/transient/lsstsw3/build/pipe_tasks
[gw0] Python 3.6.0 |Continuum Analytics, Inc.| (default, Dec 23 2016, 13:19:00) -- [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]
[gw1] Python 3.6.0 |Continuum Analytics, Inc.| (default, Dec 23 2016, 13:19:00) -- [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]
gw0 [5] / gw1 [5]
scheduling tests via LoadScopeScheduling
tests/test_register.py::RegisterTestCase::testRegister
[gw1] PASSED tests/test_register.py::RegisterTestCase::testRegister
tests/test_register.py::RegisterTestCase::testRejection
[gw1] PASSED tests/test_register.py::RegisterTestCase::testRejection
tests/test_register.py::MyMemoryTestCase::testFileDescriptorLeaks <- ../../../../../../Users/timj/work/lsstsw3/stack/DarwinX86/utils/13.0-9-gf29e843+2/python/lsst/utils/tests.py
[gw1] PASSED tests/test_register.py::MyMemoryTestCase::testFileDescriptorLeaks <- ../../../../../../Users/timj/work/lsstsw3/stack/DarwinX86/utils/13.0-9-gf29e843+2/python/lsst/utils/tests.py
tests/test_register.py::MyMemoryTestCase::testLeaks <- ../../../../../../Users/timj/work/lsstsw3/stack/DarwinX86/utils/13.0-9-gf29e843+2/python/lsst/utils/tests.py
tests/test_register.py
[gw1] PASSED tests/test_register.py::MyMemoryTestCase::testLeaks <- ../../../../../../Users/timj/work/lsstsw3/stack/DarwinX86/utils/13.0-9-gf29e843+2/python/lsst/utils/tests.py
[gw0] FAILED tests/test_register.py
```
(the failure is simply that this particular file has a flake8 issue).
I am wondering if the `pytest-flake8` plugin is not correctly returning scoping information to the scheduler in a similar way to it not working properly with `pytest-randomly` (tholo/pytest-flake8#26), even so, how come `-n 2` is fine?
When it hangs this is the stack trace:
```
scheduling tests via LoadScopeScheduling
^C
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! KeyboardInterrupt !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
config = <_pytest.config.Config object at 0x10ae57b00>
doit = <function _main at 0x10ae2f378>
def wrap_session(config, doit):
"""Skeleton command line program"""
session = Session(config)
session.exitstatus = EXIT_OK
initstate = 0
try:
try:
config._do_configure()
initstate = 1
config.hook.pytest_sessionstart(session=session)
initstate = 2
> session.exitstatus = doit(config, session) or 0
../../stack/DarwinX86/pytest/3.2.0/lib/python/pytest-3.2.0-py3.6.egg/_pytest/main.py:110:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
config = <_pytest.config.Config object at 0x10ae57b00>
session = <Session 'pipe_tasks'>
def _main(config, session):
""" default command line protocol for initialization, session,
running tests and reporting. """
config.hook.pytest_collection(session=session)
> config.hook.pytest_runtestloop(session=session)
../../stack/DarwinX86/pytest/3.2.0/lib/python/pytest-3.2.0-py3.6.egg/_pytest/main.py:146:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <_HookCaller 'pytest_runtestloop'>
kwargs = {'__multicall__': <_MultiCall 0 results, 1 meths, kwargs={'session': <Session 'pipe_tasks'>, '__multicall__': <_MultiCall 0 results, 1 meths, kwargs={...}>}>, 'session': <Session 'pipe_tasks'>}
def __call__(self, **kwargs):
assert not self.is_historic()
> return self._hookexec(self, self._nonwrappers + self._wrappers, kwargs)
../../stack/DarwinX86/pytest/3.2.0/lib/python/pytest-3.2.0-py3.6.egg/_pytest/vendored_packages/pluggy.py:745:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <_pytest.config.PytestPluginManager object at 0x10ac03d68>
hook = <_HookCaller 'pytest_runtestloop'>
methods = [<_pytest.vendored_packages.pluggy.HookImpl object at 0x10ae666d8>]
kwargs = {'__multicall__': <_MultiCall 0 results, 1 meths, kwargs={'session': <Session 'pipe_tasks'>, '__multicall__': <_MultiCall 0 results, 1 meths, kwargs={...}>}>, 'session': <Session 'pipe_tasks'>}
def _hookexec(self, hook, methods, kwargs):
# called from all hookcaller instances.
# enable_tracing will set its own wrapping function at self._inner_hookexec
> return self._inner_hookexec(hook, methods, kwargs)
../../stack/DarwinX86/pytest/3.2.0/lib/python/pytest-3.2.0-py3.6.egg/_pytest/vendored_packages/pluggy.py:339:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
hook = <_HookCaller 'pytest_runtestloop'>
methods = [<_pytest.vendored_packages.pluggy.HookImpl object at 0x10ae666d8>]
kwargs = {'__multicall__': <_MultiCall 0 results, 1 meths, kwargs={'session': <Session 'pipe_tasks'>, '__multicall__': <_MultiCall 0 results, 1 meths, kwargs={...}>}>, 'session': <Session 'pipe_tasks'>}
self._inner_hookexec = lambda hook, methods, kwargs: \
> _MultiCall(methods, kwargs, hook.spec_opts).execute()
../../stack/DarwinX86/pytest/3.2.0/lib/python/pytest-3.2.0-py3.6.egg/_pytest/vendored_packages/pluggy.py:334:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <_MultiCall 0 results, 1 meths, kwargs={'session': <Session 'pipe_tasks'>, '__multicall__': <_MultiCall 0 results, 1 meths, kwargs={...}>}>
def execute(self):
all_kwargs = self.kwargs
self.results = results = []
firstresult = self.specopts.get("firstresult")
while self.hook_impls:
hook_impl = self.hook_impls.pop()
try:
args = [all_kwargs[argname] for argname in hook_impl.argnames]
except KeyError:
for argname in hook_impl.argnames:
if argname not in all_kwargs:
raise HookCallError(
"hook call must provide argument %r" % (argname,))
if hook_impl.hookwrapper:
return _wrapped_call(hook_impl.function(*args), self.execute)
> res = hook_impl.function(*args)
../../stack/DarwinX86/pytest/3.2.0/lib/python/pytest-3.2.0-py3.6.egg/_pytest/vendored_packages/pluggy.py:614:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <xdist.dsession.DSession object at 0x10b21a1d0>
def pytest_runtestloop(self):
self.sched = self.config.hook.pytest_xdist_make_scheduler(
config=self.config,
log=self.log
)
assert self.sched is not None
self.shouldstop = False
while not self.session_finished:
> self.loop_once()
/Users/timj/work/lsstsw3/stack/DarwinX86/pytest_xdist/1.19.1/lib/python/pytest_xdist-1.19.2.dev0+g459d52e.d20170815-py3.6.egg/xdist/dsession.py:114:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <xdist.dsession.DSession object at 0x10b21a1d0>
def loop_once(self):
"""Process one callback from one of the slaves."""
while 1:
try:
> eventcall = self.queue.get(timeout=2.0)
/Users/timj/work/lsstsw3/stack/DarwinX86/pytest_xdist/1.19.1/lib/python/pytest_xdist-1.19.2.dev0+g459d52e.d20170815-py3.6.egg/xdist/dsession.py:124:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <queue.Queue object at 0x10b21a240>, block = True, timeout = 2.0
def get(self, block=True, timeout=None):
'''Remove and return an item from the queue.
If optional args 'block' is true and 'timeout' is None (the default),
block if necessary until an item is available. If 'timeout' is
a non-negative number, it blocks at most 'timeout' seconds and raises
the Empty exception if no item was available within that time.
Otherwise ('block' is false), return an item if one is immediately
available, else raise the Empty exception ('timeout' is ignored
in that case).
'''
with self.not_empty:
if not block:
if not self._qsize():
raise Empty
elif timeout is None:
while not self._qsize():
self.not_empty.wait()
elif timeout < 0:
raise ValueError("'timeout' must be a non-negative number")
else:
endtime = time() + timeout
while not self._qsize():
remaining = endtime - time()
if remaining <= 0.0:
raise Empty
> self.not_empty.wait(remaining)
/Users/timj/work/lsstsw3/miniconda/lib/python3.6/queue.py:173:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <Condition(<unlocked _thread.lock object at 0x10b1ac4e0>, 0)>
timeout = 1.999992159951944
def wait(self, timeout=None):
"""Wait until notified or until a timeout occurs.
If the calling thread has not acquired the lock when this method is
called, a RuntimeError is raised.
This method releases the underlying lock, and then blocks until it is
awakened by a notify() or notify_all() call for the same condition
variable in another thread, or until the optional timeout occurs. Once
awakened or timed out, it re-acquires the lock and returns.
When the timeout argument is present and not None, it should be a
floating point number specifying a timeout for the operation in seconds
(or fractions thereof).
When the underlying lock is an RLock, it is not released using its
release() method, since this may not actually unlock the lock when it
was acquired multiple times recursively. Instead, an internal interface
of the RLock class is used, which really unlocks it even when it has
been recursively acquired several times. Another internal interface is
then used to restore the recursion level when the lock is reacquired.
"""
if not self._is_owned():
raise RuntimeError("cannot wait on un-acquired lock")
waiter = _allocate_lock()
waiter.acquire()
self._waiters.append(waiter)
saved_state = self._release_save()
gotit = False
try: # restore state no matter what (e.g., KeyboardInterrupt)
if timeout is None:
waiter.acquire()
gotit = True
else:
if timeout > 0:
> gotit = waiter.acquire(True, timeout)
E KeyboardInterrupt
/Users/timj/work/lsstsw3/miniconda/lib/python3.6/threading.py:299: KeyboardInterrupt
======================== no tests ran in 65.08 seconds =========================
```
| open | 2017-09-03T15:59:06Z | 2017-09-03T16:28:02Z | https://github.com/pytest-dev/pytest-xdist/issues/230 | [
"bug"
] | timj | 0 |
KaiyangZhou/deep-person-reid | computer-vision | 111 | Need to download datasets ? | Hello,
Have a simple question on how to use the pretrained versions on a given dataset or my own one.
I got the weights "se_resnet50_fc512_market_xent.pth.tar"
And run :
python train_imgreid_xent.py -t market1501
-s market1501
--height 256
--width 128
--test-batch-size 100
--evaluate
-a se_resnet50_fc512
--load-weights se_resnet50_fc512_market_xent\se_resnet50_fc512_market_xent.pth.tar --save-dir log\eval-resnet50
--gpu-devices 0
But get the following error, as if the dataset was not found.
Traceback (most recent call last):
File "train_imgreid_xent.py", line 257, in <module>
main()
File "train_imgreid_xent.py", line 53, in main
dm = ImageDataManager(use_gpu, **image_dataset_kwargs(args))
File "C:\Users\César Bouyssi\Desktop\FitnessPlus\re_Identification\deep-person-reid\torchreid\data_manager.py", line 68, in __init__
cuhk03_classic_split=cuhk03_classic_split, market1501_500k=market1501_500k
File "C:\Users\César Bouyssi\Desktop\FitnessPlus\re_Identification\deep-person-reid\torchreid\datasets\__init__.py", line 47, in init_imgreid_dataset
return __imgreid_factory[name](**kwargs)
File "C:\Users\César Bouyssi\Desktop\FitnessPlus\re_Identification\deep-person-reid\torchreid\datasets\market1501.py", line 45, in __init__
self._check_before_run()
File "C:\Users\César Bouyssi\Desktop\FitnessPlus\re_Identification\deep-person-reid\torchreid\datasets\market1501.py", line 68, in _check_before_run
raise RuntimeError('"{}" is not available'.format(self.dataset_dir))
RuntimeError: "data\market1501" is not available
Should I get it manually, and put it in the folder "data\market1501" ?
Now what if I want to try it on a dataset of mine ?
Looking forward to your answer. Thanks | closed | 2019-02-07T11:26:55Z | 2019-02-12T19:57:05Z | https://github.com/KaiyangZhou/deep-person-reid/issues/111 | [] | cbouyssi | 6 |
jmcnamara/XlsxWriter | pandas | 573 | In-memory workbooks are not compressed on close() | Using XlsxWriter with the `in_memory` option results in files which are not properly compressed.
The issue is caused by XlsxWriter constructing its own `ZipInfo` objects and using `ZipFile.writestr()` to write them without specifying the compression type. It's not documented, but from the Python `zipfile` module's source I was able to tell that in this case `ZipInfo` files do not inherit the `ZipFile`'s compression and default to `ZIP_STORED`. As the `ZipFile` is instantiated with `ZIP_DEFLATED` I assume this is not intentional.
I've verified the problem occurs with XlsxWriter 1.1.1 (and the latest development version too) both with Python version 2.7.15 and 3.6.6.
A short script that can be used to reproduce the problem:
```python
from xlsxwriter import Workbook
with open("sample.xlsx", "wb") as f, Workbook(f, {"in_memory": True}) as wb:
ws = wb.add_worksheet()
ws.write_number("A1", 0)
```
I've first noticed the size differences when comparing generated files with ones created by Excel. But it can be verified with the `zipinfo` command:
```
$ zipinfo sample.xlsx
Archive: sample.xlsx
Zip file size: 13503 bytes, number of entries: 9
?rw------- 2.0 unx 516 b- stor 80-Jan-01 00:00 xl/worksheets/sheet1.xml
?rw------- 2.0 unx 550 b- stor 80-Jan-01 00:00 xl/workbook.xml
?rw------- 2.0 unx 784 b- stor 80-Jan-01 00:00 docProps/app.xml
?rw------- 2.0 unx 592 b- stor 80-Jan-01 00:00 docProps/core.xml
?rw------- 2.0 unx 1031 b- stor 80-Jan-01 00:00 [Content_Types].xml
?rw------- 2.0 unx 867 b- stor 80-Jan-01 00:00 xl/styles.xml
?rw------- 2.0 unx 6994 b- stor 80-Jan-01 00:00 xl/theme/theme1.xml
?rw------- 2.0 unx 587 b- stor 80-Jan-01 00:00 _rels/.rels
?rw------- 2.0 unx 556 b- stor 80-Jan-01 00:00 xl/_rels/workbook.xml.rels
9 files, 12477 bytes uncompressed, 12477 bytes compressed: 0.0%
```
The expected compression level is greater than 0%, also the files are listed with `STORE` compression. | closed | 2018-10-18T15:41:21Z | 2018-10-20T14:14:04Z | https://github.com/jmcnamara/XlsxWriter/issues/573 | [
"bug"
] | theag3nt | 3 |
nonebot/nonebot2 | fastapi | 3,238 | Plugin: nonebot_plugin_dingzhen | ### PyPI 项目名
nonebot_plugin_dingzhen
### 插件 import 包名
nonebot_plugin_dingzhen
### 标签
[{"label":"丁真","color":"#ff337b"},{"label":"语音合成","color":"#1942ff"},{"label":"QQ","color":"#07ede9"}]
### 插件配置项
```dotenv
```
### 插件测试
- [ ] 如需重新运行插件测试，请勾选左侧勾选框 | closed | 2025-01-05T05:36:36Z | 2025-01-05T06:12:47Z | https://github.com/nonebot/nonebot2/issues/3238 | [
"Plugin",
"Publish"
] | Pochinki98 | 1 |
huggingface/peft | pytorch | 1,504 | Feature Request: Integrate Lora+/different learning rates for adapter matrices A and B | ### Feature request
[LoRA+: Efficient Low Rank Adaptation of Large Models](https://arxiv.org/abs/2402.12354) builds on LoRA " by setting different learning rates for the LoRA adapter matrices A and B with a well-chosen ratio", which they argue provides performance improvements, speedups, and no increase in computational cost.
Code is available at https://github.com/nikhil-ghosh-berkeley/loraplus.
### Motivation
If it is true that using a ratio between the learning rates provides improvements at no cost, then having this as a new default could be broadly helpful.
### Your contribution
Just wanted to point to https://github.com/nikhil-ghosh-berkeley/loraplus/blob/main/loraplus.py#L31 and https://github.com/nikhil-ghosh-berkeley/loraplus/blob/main/loraplus.py#L131, which seem to provide pretty much drop-in replacements for 🤗 Trainer.
They explain usage in the README also, at https://github.com/nikhil-ghosh-berkeley/loraplus?tab=readme-ov-file#usage, showing how to create a Trainer, or an Optimizer, and the new hyperparameters introduced. | closed | 2024-02-22T17:49:57Z | 2024-07-29T10:51:50Z | https://github.com/huggingface/peft/issues/1504 | [] | cleong110 | 22 |
deepfakes/faceswap | deep-learning | 853 | ValueError: Error initializing Aligner | **Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Steps to reproduce the behavior:
1. Download Releases from https://github.com/deepfakes/faceswap/releases/download/v1.0.0/faceswap_setup_x64.exe
2. Install
3. Open FaceSwap and click Extract
4. Get this error
**Screenshots**
**Expected behavior**
The output files should appear in the selected folder
**Desktop (please complete the following information):**
- OS: [Windows 10.17763]
- Browser [Chrome]
- Version [76.0.3809.132]
**Additional context**
08/31/2019 21:33:52 MainProcess MainThread logger log_setup INFO Log level set to: INFO
08/31/2019 21:33:54 MainProcess MainThread extract __init__ INFO Output Directory: C:\Users\ppepp\Downloads\test
08/31/2019 21:33:54 MainProcess MainThread fsmedia check_input_folder INFO Input Video: C:\Users\ppepp\Desktop\test.mp4
08/31/2019 21:33:54 MainProcess MainThread plugin_loader _import INFO Loading Detect from S3Fd plugin...
08/31/2019 21:33:54 MainProcess MainThread plugin_loader _import INFO Loading Align from Fan plugin...
08/31/2019 21:33:54 MainProcess MainThread pipeline set_parallel_processing WARNING Not enough free VRAM for parallel processing. Switching to serial
08/31/2019 21:33:54 MainProcess MainThread extract process INFO Starting, this may take a while...
08/31/2019 21:33:57 Detector.run MainThread s3fd initialize INFO Initializing S3FD Detector...
08/31/2019 21:33:57 Detector.run MainThread deprecation_wrapper __getattr__ WARNING From C:\Users\ppepp\faceswap\plugins\extract\detect\s3fd.py:142: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n
08/31/2019 21:33:57 Detector.run MainThread deprecation_wrapper __getattr__ WARNING From C:\Users\ppepp\faceswap\plugins\extract\detect\s3fd.py:143: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.\n
08/31/2019 21:33:58 Detector.run MainThread deprecation_wrapper __getattr__ WARNING From C:\Users\ppepp\faceswap\plugins\extract\detect\s3fd.py:165: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n
08/31/2019 21:33:58 Detector.run MainThread deprecation_wrapper __getattr__ WARNING From C:\Users\ppepp\faceswap\plugins\extract\detect\s3fd.py:172: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n
08/31/2019 21:34:07 Detector.run MainThread s3fd initialize WARNING You are running s3fd with 1743MB VRAM. The model is optimized for 4224MB VRAM. Detection should still run but you may get warnings/errors
08/31/2019 21:34:07 Detector.run MainThread s3fd initialize INFO Initialized S3FD Detector.
08/31/2019 21:36:04 Aligner.run MainThread fan initialize INFO Initializing Face Alignment Network...
08/31/2019 21:36:04 Aligner.run MainThread deprecation_wrapper __getattr__ WARNING From C:\Users\ppepp\faceswap\plugins\extract\align\fan.py:206: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n
08/31/2019 21:36:04 Aligner.run MainThread deprecation_wrapper __getattr__ WARNING From C:\Users\ppepp\faceswap\plugins\extract\align\fan.py:207: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.\n
08/31/2019 21:36:08 Aligner.run MainThread deprecation_wrapper __getattr__ WARNING From C:\Users\ppepp\faceswap\plugins\extract\align\fan.py:219: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n
08/31/2019 21:36:08 Aligner.run MainThread deprecation_wrapper __getattr__ WARNING From C:\Users\ppepp\faceswap\plugins\extract\align\fan.py:221: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n
08/31/2019 21:36:16 Aligner.run MainThread _base run ERROR Caught exception in child process: 11128
08/31/2019 21:36:16 Aligner.run MainThread _base run ERROR Traceback:
Traceback (most recent call last):
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\client\session.py", line 1356, in _do_call
return fn(*args)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\client\session.py", line 1341, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\client\session.py", line 1429, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node fa/convolution}}]]
[[fa/transpose_647/_3]]
(1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node fa/convolution}}]]
0 successful operations.
0 derived errors ignored.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\ppepp\faceswap\plugins\extract\align\_base.py", line 112, in run
self.align(*args, **kwargs)
File "C:\Users\ppepp\faceswap\plugins\extract\align\_base.py", line 127, in align
self.initialize(*args, **kwargs)
File "C:\Users\ppepp\faceswap\plugins\extract\align\fan.py", line 47, in initialize
raise err
File "C:\Users\ppepp\faceswap\plugins\extract\align\fan.py", line 41, in initialize
self.model = FAN(self.model_path, ratio=tf_ratio)
File "C:\Users\ppepp\faceswap\plugins\extract\align\fan.py", line 199, in __init__
self.session = self.set_session(ratio)
File "C:\Users\ppepp\faceswap\plugins\extract\align\fan.py", line 227, in set_session
session.run(self.output, feed_dict={self.input: placeholder})
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\client\session.py", line 950, in run
run_metadata_ptr)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\client\session.py", line 1173, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\client\session.py", line 1350, in _do_run
run_metadata)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\client\session.py", line 1370, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[node fa/convolution (defined at C:\Users\ppepp\faceswap\plugins\extract\align\fan.py:211) ]]
[[fa/transpose_647/_3]]
(1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[node fa/convolution (defined at C:\Users\ppepp\faceswap\plugins\extract\align\fan.py:211) ]]
0 successful operations.
0 derived errors ignored.
Original stack trace for 'fa/convolution':
File "<string>", line 1, in <module>
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\multiprocessing\spawn.py", line 118, in _main
return self._bootstrap()
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\multiprocessing\process.py", line 258, in _bootstrap
self.run()
File "C:\Users\ppepp\faceswap\lib\multithreading.py", line 362, in run
super().run()
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\multiprocessing\process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "C:\Users\ppepp\faceswap\plugins\extract\align\_base.py", line 112, in run
self.align(*args, **kwargs)
File "C:\Users\ppepp\faceswap\plugins\extract\align\_base.py", line 127, in align
self.initialize(*args, **kwargs)
File "C:\Users\ppepp\faceswap\plugins\extract\align\fan.py", line 41, in initialize
self.model = FAN(self.model_path, ratio=tf_ratio)
File "C:\Users\ppepp\faceswap\plugins\extract\align\fan.py", line 196, in __init__
self.graph = self.load_graph()
File "C:\Users\ppepp\faceswap\plugins\extract\align\fan.py", line 211, in load_graph
self.tf.import_graph_def(graph_def, name="fa")
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\framework\importer.py", line 443, in import_graph_def
_ProcessNewOps(graph)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\framework\importer.py", line 236, in _ProcessNewOps
for new_op in graph._add_new_tf_operations(compute_devices=False): # pylint: disable=protected-access
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\framework\ops.py", line 3751, in _add_new_tf_operations
for c_op in c_api_util.new_tf_operations(self)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\framework\ops.py", line 3751, in <listcomp>
for c_op in c_api_util.new_tf_operations(self)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\framework\ops.py", line 3641, in _create_op_from_tf_operation
ret = Operation(c_op, self)
File "C:\Users\ppepp\MiniConda3\envs\faceswap\lib\site-packages\tensorflow\python\framework\ops.py", line 2005, in __init__
self._traceback = tf_stack.extract_stack()
08/31/2019 21:36:18 MainProcess MainThread cli execute_script ERROR Got Exception on main handler:
Traceback (most recent call last):
File "C:\Users\ppepp\faceswap\lib\cli.py", line 125, in execute_script
process.process()
File "C:\Users\ppepp\faceswap\scripts\extract.py", line 62, in process
self.run_extraction()
File "C:\Users\ppepp\faceswap\scripts\extract.py", line 189, in run_extraction
self.extractor.launch()
File "C:\Users\ppepp\faceswap\plugins\extract\pipeline.py", line 178, in launch
self.launch_aligner()
File "C:\Users\ppepp\faceswap\plugins\extract\pipeline.py", line 206, in launch_aligner
raise ValueError("Error initializing Aligner")
ValueError: Error initializing Aligner
08/31/2019 21:36:18 MainProcess MainThread cli execute_script CRITICAL An unexpected crash has occurred. Crash report written to 'C:\Users\ppepp\faceswap\crash_report.2019.08.31.213618860456.log'. Please verify you are running the latest version of faceswap before reporting
| closed | 2019-08-31T14:43:42Z | 2019-12-02T00:54:44Z | https://github.com/deepfakes/faceswap/issues/853 | [] | peppapighs | 8 |
Kanaries/pygwalker | plotly | 644 | Support for pygwalker in Reflex | Reflex (https://reflex.dev) is the up and coming Python framework for web apps with 20k stars on Github. Would love to see an integration so we can use pygwalker in Reflex apps.
| open | 2024-10-15T17:28:38Z | 2024-10-20T14:22:10Z | https://github.com/Kanaries/pygwalker/issues/644 | [
"enhancement"
] | tgberkeley | 0 |
Significant-Gravitas/AutoGPT | python | 9,025 | Marketplace - agent page - update font of description header |
### Describe your issue.
<img width="598" alt="Screenshot 2024-12-17 at 18 53 43" src="https://github.com/user-attachments/assets/32fe7be0-ef3e-400b-a750-372381a8d177" />
Please update font to the "p-ui-medium" style in the typography sheet
Update font to the following:
font-family: Geist;
font-size: 16px;
font-weight: 500;
line-height: 24px;
text-align: left;
text-underline-position: from-font;
text-decoration-skip-ink: none;
**Update color to:**
background: var(--neutral-800, #262626);
| closed | 2024-12-17T10:54:58Z | 2024-12-20T13:46:28Z | https://github.com/Significant-Gravitas/AutoGPT/issues/9025 | [
"good first issue",
"UI",
"platform/frontend"
] | ograce1421 | 0 |
holoviz/panel | jupyter | 7,506 | AttributeError: 'Button' object has no attribute 'deepcopy' - when running Component Gallery > Widgets > Button in Panel 1.5.4 documentation | #### ALL software version info
The error does appear in the Panel documentation, when tryin to run the code examples in https://panel.holoviz.org/reference/widgets/Button.html via "Run cell" button. At this time the most recent version of Panel is 1.5.4.
<details>
<summary>Software Version Info</summary>
```plaintext
Chrome 130.0.6723.117 (64-bit)
Windows 10 Home 22H2 (64-bit)
```
</details>
#### Description of expected behavior and the observed behavior
I wanted to see how the Button widget would behave with Python backend behind it, so on the [Component Gallery > Widgets > Button](https://panel.holoviz.org/reference/widgets/Button.html) page in Panel v1.5.4 user documentation I have clicked on "Run cell" button in the first cell, then clicked it again when asked "Click again to proceed".
Most cells executed just fine, and showed "Executed successfully" info message.
The three last cells however showed instead the following error message "AttributeError: 'Button' object has no attribute 'deepcopy'"
#### Example code cell in the documentation that exhibits this behavior
```python
pn.Row(
pn.widgets.Button(icon='alert-triangle-filled', button_type='warning', name='WARNING'),
pn.widgets.Button(icon='bug', button_type='danger', name='Error')
)
```
#### Stack traceback and/or browser JavaScript console output
```
AttributeError: 'Button' object has no attribute 'deepcopy'
```
#### Screenshots or screencasts of the bug in action
- [ ] I may be interested in making a pull request to address this
| open | 2024-11-19T22:33:45Z | 2024-11-19T22:33:45Z | https://github.com/holoviz/panel/issues/7506 | [] | jnareb | 0 |
sqlalchemy/alembic | sqlalchemy | 1,151 | Comparing computed fields throws warn | I'm not sure if this is a bug or a mistake caused by me, but I wanted to share it here because I couldn't find a proper solution.
I have a computed `tsvector` field in my database as you can see in the section of the migration file and model I shared below.
```python
def upgrade():
op.create_table(
"a_table",
sa.Column("Column1", sa.String(), nullable=False),
sa.Column("Column2", sa.ARRAY(sa.String(), dimensions=1), nullable=True),
sa.Column("Column3", sa.ARRAY(sa.String(), dimensions=1), nullable=True),
sa.Column("Column4", sa.ARRAY(sa.String(), dimensions=1), nullable=True),
sa.Column("Column5", sa.ARRAY(sa.String(), dimensions=1), nullable=True),
sa.Column("Column6", sa.ARRAY(sa.String(), dimensions=1), nullable=True),
)
op.execute(
"""
CREATE OR REPLACE FUNCTION immutable_array_to_string(text[], text)
RETURNS text as $$ SELECT array_to_string($1, $2); $$
LANGUAGE sql IMMUTABLE"""
)
op.execute(
"""
ALTER TABLE elementsearchindex
ADD COLUMN new_column tsvector GENERATED ALWAYS
AS (to_tsvector('english', Column1
|| ' ' || immutable_array_to_string(coalesce(Column2, '{}'), ' ')
|| ' ' || immutable_array_to_string(coalesce(Column3, '{}'), ' ')
|| ' ' || immutable_array_to_string(coalesce(Column4, '{}'), ' ')
|| ' ' || immutable_array_to_string(coalesce(Column5, '{}'), ' ')
|| ' ' || immutable_array_to_string(coalesce(Column6, '{}'), ' ')
)
) STORED
"""
)
```
```python
class TSVector(TypeDecorator):
impl = TSVECTOR
class ATable(Base):
Column1 = Column(String, nullable=False, index=True)
Column2 = Column(ARRAY(String, dimensions=1), nullable=True, index=True)
Column3 = Column(ARRAY(String, dimensions=1), nullable=True, index=True)
Column4 = Column(ARRAY(String, dimensions=1), nullable=True, index=True)
Column5 = Column(ARRAY(String, dimensions=1), nullable=True, index=True)
Column6 = Column(ARRAY(String, dimensions=1), nullable=True, index=True)
new_column = Column(
TSVector(),
Computed(
"""to_tsvector('english', Column1
|| ' ' || immutable_array_to_string(coalesce(Column2, '{}'), ' ')
|| ' ' || immutable_array_to_string(coalesce(Column3, '{}'), ' ')
|| ' ' || immutable_array_to_string(coalesce(Column4, '{}'), ' ')
|| ' ' || immutable_array_to_string(coalesce(Column5, '{}'), ' ')
|| ' ' || immutable_array_to_string(coalesce(Column6, '{}'), ' ')
)""",
persisted=True,
),
nullable=True,
index=True,
)
```
I do not have any problems during the upgrade or downgrade. Likewise, the system is working properly. But whenever I want to do any database update I see the following warning.
```
alembic/autogenerate/compare.py:1090: UserWarning: Computed default on a_table.new_field cannot be modified
```
I can see the need of throwing message when there is a change but no idea why comparison think there are some changes.
**Versions.**
- OS: Monterey 12.1
- Python: 3.10.6
- Alembic: 1.8.1
- SQLAlchemy: 1.4.35
- Database: Postgres 14
Thank you!
| closed | 2023-01-10T16:51:37Z | 2024-07-18T12:22:39Z | https://github.com/sqlalchemy/alembic/issues/1151 | [
"bug",
"autogenerate - defaults",
"autogenerate - detection",
"postgresql",
"cant reproduce"
] | hevalhazalkurt | 4 |
gunthercox/ChatterBot | machine-learning | 2,080 | Failed to install ChatterBot (v1.1.0) through PyCharm. | Trying to install ChatterBot (v1.1.0) through PyCharm (Community Edition 2019.3.3 x64) and installed Python version is -- v3.8.6
**D:\Python_project>python --version
Python 3.8.6**
The installing is failing and getting error as --
Collecting ChatterBot==1.1.0
Using cached ChatterBot-1.1.0-py2.py3-none-any.whl (63 kB)
Requirement already satisfied: pytz in d:\software\python3.8.6-64\lib\site-packages (from ChatterBot==1.1.0) (2020.4)
Requirement already satisfied: nltk<4.0,>=3.2 in d:\software\python3.8.6-64\lib\site-packages (from ChatterBot==1.1.0) (3.5)
Requirement already satisfied: mathparse<0.2,>=0.1 in d:\software\python3.8.6-64\lib\site-packages (from ChatterBot==1.1.0) (0.1.2)
Requirement already satisfied: pint>=0.8.1 in d:\software\python3.8.6-64\lib\site-packages (from ChatterBot==1.1.0) (0.16.1)
Requirement already satisfied: regex in d:\software\python3.8.6-64\lib\site-packages (from nltk<4.0,>=3.2->ChatterBot==1.1.0) (2020.11.13)
Requirement already satisfied: joblib in d:\software\python3.8.6-64\lib\site-packages (from nltk<4.0,>=3.2->ChatterBot==1.1.0) (0.17.0)
Requirement already satisfied: tqdm in d:\software\python3.8.6-64\lib\site-packages (from nltk<4.0,>=3.2->ChatterBot==1.1.0) (4.54.1)
Requirement already satisfied: click in d:\software\python3.8.6-64\lib\site-packages (from nltk<4.0,>=3.2->ChatterBot==1.1.0) (7.1.2)
Requirement already satisfied: packaging in d:\software\python3.8.6-64\lib\site-packages (from pint>=0.8.1->ChatterBot==1.1.0) (20.7)
Requirement already satisfied: pyparsing>=2.0.2 in d:\software\python3.8.6-64\lib\site-packages (from packaging->pint>=0.8.1->ChatterBot==1.1.0) (2.4.7)
Collecting python-dateutil<2.9,>=2.8
Using cached python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB)
Requirement already satisfied: six>=1.5 in d:\software\python3.8.6-64\lib\site-packages (from python-dateutil<2.9,>=2.8->ChatterBot==1.1.0) (1.15.0)
Collecting pyyaml<5.4,>=5.3
Using cached PyYAML-5.3.1-cp38-cp38-win_amd64.whl (219 kB)
Collecting spacy<2.2,>=2.1
Using cached spacy-2.1.9.tar.gz (30.7 MB)
Installing build dependencies: started
Installing build dependencies: still running...
Installing build dependencies: finished with status 'error'
DEPRECATION: The -b/--build/--build-dir/--build-directory option is deprecated and has no effect anymore. pip 21.1 will remove support for this functionality. A possible replacement is use the TMPDIR/TEMP/TMP environment variable, possibly combined with --no-clean. You can find discussion regarding this at https://github.com/pypa/pip/issues/8333.
ERROR: Command errored out with exit status 1:
command: 'D:\Python_project\venv\Scripts\python.exe' 'D:\software\python3.8.6-64\lib\site-packages\pip' install --ignore-installed --no-user --prefix 'C:\Users\bgh25154\AppData\Local\Temp\pip-build-env-_yz43l6b\overlay' --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- setuptools 'wheel>0.32.0,<0.33.0' Cython 'cymem>=2.0.2,<2.1.0' 'preshed>=2.0.1,<2.1.0' 'murmurhash>=0.28.0,<1.1.0' 'thinc>=7.0.8,<7.1.0'
cwd: None
Complete output (286 lines):
Collecting cymem<2.1.0,>=2.0.2
Using cached cymem-2.0.4-cp38-cp38-win_amd64.whl (36 kB)
Collecting Cython
Using cached Cython-0.29.21-cp38-cp38-win_amd64.whl (1.7 MB)
Collecting murmurhash<1.1.0,>=0.28.0
Using cached murmurhash-1.0.4-cp38-cp38-win_amd64.whl (21 kB)
Collecting preshed<2.1.0,>=2.0.1
Using cached preshed-2.0.1.tar.gz (113 kB)
Collecting setuptools
Using cached setuptools-50.3.2-py3-none-any.whl (785 kB)
Collecting thinc<7.1.0,>=7.0.8
Using cached thinc-7.0.8.tar.gz (1.9 MB)
Collecting wheel<0.33.0,>0.32.0
Using cached wheel-0.32.3-py2.py3-none-any.whl (21 kB)
Collecting blis<0.3.0,>=0.2.1
Using cached blis-0.2.4.tar.gz (1.5 MB)
Collecting numpy>=1.7.0
Using cached numpy-1.19.4-cp38-cp38-win_amd64.whl (13.0 MB)
Collecting plac<1.0.0,>=0.9.6
Using cached plac-0.9.6-py2.py3-none-any.whl (20 kB)
Collecting srsly<1.1.0,>=0.0.6
Using cached srsly-1.0.4-cp38-cp38-win_amd64.whl (287 kB)
Collecting tqdm<5.0.0,>=4.10.0
Using cached tqdm-4.54.1-py2.py3-none-any.whl (69 kB)
Collecting wasabi<1.1.0,>=0.0.9
Using cached wasabi-0.8.0-py3-none-any.whl (23 kB)
Building wheels for collected packages: preshed, thinc, blis
Building wheel for preshed (setup.py): started
Building wheel for preshed (setup.py): finished with status 'error'
ERROR: Command errored out with exit status 1:
command: 'D:\Python_project\venv\Scripts\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\preshed_ce345c272e0544caae37430a8c27ad64\\setup.py'"'"'; __file__='"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\preshed_ce345c272e0544caae37430a8c27ad64\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\bgh25154\AppData\Local\Temp\pip-wheel-_my3konf'
cwd: C:\Users\bgh25154\AppData\Local\Temp\pip-install-p0xvvzvq\preshed_ce345c272e0544caae37430a8c27ad64\
Complete output (23 lines):
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.8
creating build\lib.win-amd64-3.8\preshed
copying preshed\about.py -> build\lib.win-amd64-3.8\preshed
copying preshed\__init__.py -> build\lib.win-amd64-3.8\preshed
creating build\lib.win-amd64-3.8\preshed\tests
copying preshed\tests\test_counter.py -> build\lib.win-amd64-3.8\preshed\tests
copying preshed\tests\test_hashing.py -> build\lib.win-amd64-3.8\preshed\tests
copying preshed\tests\test_pop.py -> build\lib.win-amd64-3.8\preshed\tests
copying preshed\tests\__init__.py -> build\lib.win-amd64-3.8\preshed\tests
copying preshed\counter.pyx -> build\lib.win-amd64-3.8\preshed
copying preshed\maps.pyx -> build\lib.win-amd64-3.8\preshed
copying preshed\counter.pxd -> build\lib.win-amd64-3.8\preshed
copying preshed\maps.pxd -> build\lib.win-amd64-3.8\preshed
copying preshed\__init__.pxd -> build\lib.win-amd64-3.8\preshed
warning: build_py: byte-compiling is disabled, skipping.
running build_ext
building 'preshed.maps' extension
error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio": https://visualstudio.microsoft.com/downloads/
----------------------------------------
ERROR: Failed building wheel for preshed
Running setup.py clean for preshed
Building wheel for thinc (setup.py): started
Building wheel for thinc (setup.py): finished with status 'error'
ERROR: Command errored out with exit status 1:
command: 'D:\Python_project\venv\Scripts\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\thinc_bd51eae97b87487ba0b69752d2e1c682\\setup.py'"'"'; __file__='"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\thinc_bd51eae97b87487ba0b69752d2e1c682\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\bgh25154\AppData\Local\Temp\pip-wheel-8wvak1rd'
cwd: C:\Users\bgh25154\AppData\Local\Temp\pip-install-p0xvvzvq\thinc_bd51eae97b87487ba0b69752d2e1c682\
Complete output (168 lines):
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.8
creating build\lib.win-amd64-3.8\thinc
copying thinc\about.py -> build\lib.win-amd64-3.8\thinc
copying thinc\api.py -> build\lib.win-amd64-3.8\thinc
copying thinc\check.py -> build\lib.win-amd64-3.8\thinc
copying thinc\compat.py -> build\lib.win-amd64-3.8\thinc
copying thinc\describe.py -> build\lib.win-amd64-3.8\thinc
copying thinc\exceptions.py -> build\lib.win-amd64-3.8\thinc
copying thinc\i2v.py -> build\lib.win-amd64-3.8\thinc
copying thinc\loss.py -> build\lib.win-amd64-3.8\thinc
copying thinc\misc.py -> build\lib.win-amd64-3.8\thinc
copying thinc\rates.py -> build\lib.win-amd64-3.8\thinc
copying thinc\t2t.py -> build\lib.win-amd64-3.8\thinc
copying thinc\t2v.py -> build\lib.win-amd64-3.8\thinc
copying thinc\v2v.py -> build\lib.win-amd64-3.8\thinc
copying thinc\__init__.py -> build\lib.win-amd64-3.8\thinc
creating build\lib.win-amd64-3.8\thinc\tests
copying thinc\tests\conftest.py -> build\lib.win-amd64-3.8\thinc\tests
copying thinc\tests\strategies.py -> build\lib.win-amd64-3.8\thinc\tests
copying thinc\tests\test_api_funcs.py -> build\lib.win-amd64-3.8\thinc\tests
copying thinc\tests\test_util.py -> build\lib.win-amd64-3.8\thinc\tests
copying thinc\tests\util.py -> build\lib.win-amd64-3.8\thinc\tests
copying thinc\tests\__init__.py -> build\lib.win-amd64-3.8\thinc\tests
creating build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_about.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_affine.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_beam_search.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_check_exceptions.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_difference.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_feature_extracter.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_hash_embed.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_imports.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_linear.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_loss.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_mem.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_model.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_ops.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_pickle.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_pooling.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_pytorch_wrapper.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_rates.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\test_rnn.py -> build\lib.win-amd64-3.8\thinc\tests\unit
copying thinc\tests\unit\__init__.py -> build\lib.win-amd64-3.8\thinc\tests\unit
creating build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\test_affine_learns.py -> build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\test_basic_tagger.py -> build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\test_batch_norm.py -> build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\test_feed_forward.py -> build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\test_mnist.py -> build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\test_pickle.py -> build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\test_roundtrip_bytes.py -> build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\test_shape_check.py -> build\lib.win-amd64-3.8\thinc\tests\integration
copying thinc\tests\integration\__init__.py -> build\lib.win-amd64-3.8\thinc\tests\integration
creating build\lib.win-amd64-3.8\thinc\tests\linear
copying thinc\tests\linear\test_avgtron.py -> build\lib.win-amd64-3.8\thinc\tests\linear
copying thinc\tests\linear\test_linear.py -> build\lib.win-amd64-3.8\thinc\tests\linear
copying thinc\tests\linear\test_sparse_array.py -> build\lib.win-amd64-3.8\thinc\tests\linear
copying thinc\tests\linear\__init__.py -> build\lib.win-amd64-3.8\thinc\tests\linear
creating build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\__init__.py -> build\lib.win-amd64-3.8\thinc\linear
creating build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\mem.py -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\pooling.py -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\train.py -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\util.py -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\vec2vec.py -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\vecs2vec.py -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\vecs2vecs.py -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\_lsuv.py -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\__init__.py -> build\lib.win-amd64-3.8\thinc\neural
creating build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\datasets.py -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\hpbff.py -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\load_nlp.py -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\visualizer.py -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\wrappers.py -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\__init__.py -> build\lib.win-amd64-3.8\thinc\extra
creating build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\affine.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\attention.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\batchnorm.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\convolution.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\difference.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\elu.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\embed.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\encoder_decoder.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\feature_extracter.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\feed_forward.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\function_layer.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\hash_embed.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\layernorm.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\maxout.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\model.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\multiheaded_attention.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\relu.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\resnet.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\rnn.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\selu.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\softmax.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\static_vectors.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
copying thinc\neural\_classes\__init__.py -> build\lib.win-amd64-3.8\thinc\neural\_classes
creating build\lib.win-amd64-3.8\thinc\extra\_vendorized
copying thinc\extra\_vendorized\keras_datasets.py -> build\lib.win-amd64-3.8\thinc\extra\_vendorized
copying thinc\extra\_vendorized\keras_data_utils.py -> build\lib.win-amd64-3.8\thinc\extra\_vendorized
copying thinc\extra\_vendorized\keras_generic_utils.py -> build\lib.win-amd64-3.8\thinc\extra\_vendorized
copying thinc\extra\_vendorized\__init__.py -> build\lib.win-amd64-3.8\thinc\extra\_vendorized
creating build\lib.win-amd64-3.8\thinc\extra\wrapt
copying thinc\extra\wrapt\decorators.py -> build\lib.win-amd64-3.8\thinc\extra\wrapt
copying thinc\extra\wrapt\importer.py -> build\lib.win-amd64-3.8\thinc\extra\wrapt
copying thinc\extra\wrapt\wrappers.py -> build\lib.win-amd64-3.8\thinc\extra\wrapt
copying thinc\extra\wrapt\__init__.py -> build\lib.win-amd64-3.8\thinc\extra\wrapt
copying thinc\linalg.pyx -> build\lib.win-amd64-3.8\thinc
copying thinc\structs.pyx -> build\lib.win-amd64-3.8\thinc
copying thinc\typedefs.pyx -> build\lib.win-amd64-3.8\thinc
copying thinc\cpu.pxd -> build\lib.win-amd64-3.8\thinc
copying thinc\linalg.pxd -> build\lib.win-amd64-3.8\thinc
copying thinc\structs.pxd -> build\lib.win-amd64-3.8\thinc
copying thinc\typedefs.pxd -> build\lib.win-amd64-3.8\thinc
copying thinc\__init__.pxd -> build\lib.win-amd64-3.8\thinc
copying thinc\compile_time_constants.pxi -> build\lib.win-amd64-3.8\thinc
copying thinc\linalg.cpp -> build\lib.win-amd64-3.8\thinc
copying thinc\structs.cpp -> build\lib.win-amd64-3.8\thinc
copying thinc\typedefs.cpp -> build\lib.win-amd64-3.8\thinc
copying thinc\linear\avgtron.pyx -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\features.pyx -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\linear.pyx -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\serialize.pyx -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\sparse.pyx -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\avgtron.pxd -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\features.pxd -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\serialize.pxd -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\sparse.pxd -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\__init__.pxd -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\avgtron.cpp -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\features.cpp -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\linear.cpp -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\serialize.cpp -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\linear\sparse.cpp -> build\lib.win-amd64-3.8\thinc\linear
copying thinc\neural\ops.pyx -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\optimizers.pyx -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\_aligned_alloc.pyx -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\cpu.pxd -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\ops.pxd -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\__init__.pxd -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\ops.cpp -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\optimizers.cpp -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\neural\_aligned_alloc.cpp -> build\lib.win-amd64-3.8\thinc\neural
copying thinc\extra\cache.pyx -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\eg.pyx -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\mb.pyx -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\search.pyx -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\cache.pxd -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\eg.pxd -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\mb.pxd -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\search.pxd -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\__init__.pxd -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\cache.cpp -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\eg.cpp -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\mb.cpp -> build\lib.win-amd64-3.8\thinc\extra
copying thinc\extra\search.cpp -> build\lib.win-amd64-3.8\thinc\extra
warning: build_py: byte-compiling is disabled, skipping.
running build_ext
error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio": https://visualstudio.microsoft.com/downloads/
----------------------------------------
ERROR: Failed building wheel for thinc
Running setup.py clean for thinc
Building wheel for blis (setup.py): started
Building wheel for blis (setup.py): finished with status 'error'
ERROR: Command errored out with exit status 1:
command: 'D:\Python_project\venv\Scripts\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\blis_2b4c26321f554dae9c96c87ec2510fbf\\setup.py'"'"'; __file__='"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\blis_2b4c26321f554dae9c96c87ec2510fbf\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\bgh25154\AppData\Local\Temp\pip-wheel-uu3mi8zn'
cwd: C:\Users\bgh25154\AppData\Local\Temp\pip-install-p0xvvzvq\blis_2b4c26321f554dae9c96c87ec2510fbf\
Complete output (23 lines):
BLIS_COMPILER? None
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.8
creating build\lib.win-amd64-3.8\blis
copying blis\about.py -> build\lib.win-amd64-3.8\blis
copying blis\benchmark.py -> build\lib.win-amd64-3.8\blis
copying blis\__init__.py -> build\lib.win-amd64-3.8\blis
creating build\lib.win-amd64-3.8\blis\tests
copying blis\tests\common.py -> build\lib.win-amd64-3.8\blis\tests
copying blis\tests\test_dotv.py -> build\lib.win-amd64-3.8\blis\tests
copying blis\tests\test_gemm.py -> build\lib.win-amd64-3.8\blis\tests
copying blis\tests\__init__.py -> build\lib.win-amd64-3.8\blis\tests
copying blis\cy.pyx -> build\lib.win-amd64-3.8\blis
copying blis\py.pyx -> build\lib.win-amd64-3.8\blis
copying blis\cy.pxd -> build\lib.win-amd64-3.8\blis
copying blis\__init__.pxd -> build\lib.win-amd64-3.8\blis
warning: build_py: byte-compiling is disabled, skipping.
running build_ext
error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio": https://visualstudio.microsoft.com/downloads/
----------------------------------------
ERROR: Failed building wheel for blis
Running setup.py clean for blis
Failed to build preshed thinc blis
Installing collected packages: numpy, cymem, wasabi, tqdm, srsly, preshed, plac, murmurhash, blis, wheel, thinc, setuptools, Cython
Running setup.py install for preshed: started
Running setup.py install for preshed: finished with status 'error'
ERROR: Command errored out with exit status 1:
command: 'D:\Python_project\venv\Scripts\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\preshed_ce345c272e0544caae37430a8c27ad64\\setup.py'"'"'; __file__='"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\preshed_ce345c272e0544caae37430a8c27ad64\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\bgh25154\AppData\Local\Temp\pip-record-0r2ojr59\install-record.txt' --single-version-externally-managed --prefix 'C:\Users\bgh25154\AppData\Local\Temp\pip-build-env-_yz43l6b\overlay' --compile --install-headers 'C:\Users\bgh25154\AppData\Local\Temp\pip-build-env-_yz43l6b\overlay\include\site\python3.8\preshed'
cwd: C:\Users\bgh25154\AppData\Local\Temp\pip-install-p0xvvzvq\preshed_ce345c272e0544caae37430a8c27ad64\
Complete output (8 lines):
running install
running build
running build_py
warning: build_py: byte-compiling is disabled, skipping.
running build_ext
building 'preshed.maps' extension
error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio": https://visualstudio.microsoft.com/downloads/
----------------------------------------
ERROR: Command errored out with exit status 1: 'D:\Python_project\venv\Scripts\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\preshed_ce345c272e0544caae37430a8c27ad64\\setup.py'"'"'; __file__='"'"'C:\\Users\\bgh25154\\AppData\\Local\\Temp\\pip-install-p0xvvzvq\\preshed_ce345c272e0544caae37430a8c27ad64\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\bgh25154\AppData\Local\Temp\pip-record-0r2ojr59\install-record.txt' --single-version-externally-managed --prefix 'C:\Users\bgh25154\AppData\Local\Temp\pip-build-env-_yz43l6b\overlay' --compile --install-headers 'C:\Users\bgh25154\AppData\Local\Temp\pip-build-env-_yz43l6b\overlay\include\site\python3.8\preshed' Check the logs for full command output.
----------------------------------------
ERROR: Command errored out with exit status 1: 'D:\Python_project\venv\Scripts\python.exe' 'D:\software\python3.8.6-64\lib\site-packages\pip' install --ignore-installed --no-user --prefix 'C:\Users\bgh25154\AppData\Local\Temp\pip-build-env-_yz43l6b\overlay' --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- setuptools 'wheel>0.32.0,<0.33.0' Cython 'cymem>=2.0.2,<2.1.0' 'preshed>=2.0.1,<2.1.0' 'murmurhash>=0.28.0,<1.1.0' 'thinc>=7.0.8,<7.1.0' Check the logs for full command output.
| closed | 2020-12-05T18:47:45Z | 2025-02-17T19:23:19Z | https://github.com/gunthercox/ChatterBot/issues/2080 | [] | krishnendudas1979 | 4 |
Lightning-AI/LitServe | api | 426 | Allow users to define a custom health check logic | <!--
⚠️ BEFORE SUBMITTING, READ:
We're excited for your request! However, here are things we are not interested in:
- Decorators.
- Doing the same thing in multiple ways.
- Adding more layers of abstraction... tree-depth should be 1 at most.
- Features that over-engineer or complicate the code internals.
- Linters, and crud that complicates projects.
-->
----
## 🚀 Feature
<!-- A clear and concise description of the feature proposal -->
Right now the health check endpoint returns "ok" when all the processes has started. There could be scenario when the LitAPI depends on another API/service (such as check if Ollama has pulled the model in background) and health-check need to account for the liveliness of that service too.
It would be great to allow users to provide a custom health logic:
```py
class MyAPI(ls.LitAPI):
def health(self):
if SOMETHING:
return A
else:
return B
```
### Motivation
<!--
Please outline the motivation for the proposal.
Is your feature request related to a problem? e.g., I'm always frustrated when [...].
If this is related to another GitHub issue, please link here too...
-->
### Pitch
<!-- A clear and concise description of what you want to happen. -->
### Alternatives
<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
### Additional context
<!-- Add any other context or screenshots about the feature request here. -->
| closed | 2025-02-12T15:59:06Z | 2025-02-19T12:26:03Z | https://github.com/Lightning-AI/LitServe/issues/426 | [
"enhancement"
] | aniketmaurya | 1 |
iMerica/dj-rest-auth | rest-api | 411 | Subclass of RegisterSerializer not saving email when overriding class variable like email = serializers.EmailField(required=False) | open | 2022-06-08T09:48:29Z | 2022-06-08T09:48:29Z | https://github.com/iMerica/dj-rest-auth/issues/411 | [] | mateoKutnjak | 0 |
|
unit8co/darts | data-science | 2,186 | [BUG] multi_models=FALSE not working for XGBOOST | **Describe the bug**
Predict covariates does not work for XGBOOST/ CATBOOST/LIGHTGBM
**To Reproduce**
Attached is the excel file that has sample data - I did spend time going through darts.utils.timeseries_generation t generate dummy data - was not very successful - so attaching the sample excel file and code snippet
```python
data = pd.read_csv('Book1.csv')
forecast_xgboost=pd.DataFrame()
train =data.iloc[:len(data)-8]
predict=data.iloc[len(data)-8:len(data)]
series = darts.TimeSeries.from_series(train['y'])
timeseries_flag_past=darts.TimeSeries.from_series(train['weekday'])
timeseries_flag_future=darts.TimeSeries.from_series(predict['weekday'])
#works fine multi-models True
model_XGB = XGBModel(lags_future_covariates=[0],output_chunk_length=7,multi_models =True)
XGB=model_XGB.fit(series,future_covariates=timeseries_flag_past)
pred_xgb = XGB.predict(n=7,future_covariates=timeseries_flag_future)
# do not work : multi - models = False
model_XGB = XGBModel(lags_future_covariates=[0],output_chunk_length=7,multi_models =False)
XGB=model_XGB.fit(series,future_covariates=timeseries_flag_past)
pred_xgb = XGB.predict(n=7,future_covariates=timeseries_flag_future)
```
**Expected behavior**
In the predict function XGBOOST does not take the future covariates and throws error-
The corresponding future_covariate of the series at index 0 isn't sufficiently long. Given horizon `n=7`, `min(lags_future_covariates)=0`, `max(lags_future_covariates)=0` and `output_chunk_length=7`, the future_covariate has to range from 95 until 101 (inclusive), but it ranges only from 101 until 108.
**System (please complete the following information):**
Python Version 3.11.5
darts version 0.25.0
[Book1.csv](https://github.com/unit8co/darts/files/14016837/Book1.csv)
[Book1.csv](https://github.com/unit8co/darts/files/14016840/Book1.csv)
**Additional context**
Nbeats - future_covariates
Nbeats API reference does not specify the covariates- still in the fit function it takes the covariates but fails in predict function
NBEATS_14=model_nbeats_14.fit(series,past_covariates=timeseries_flag_past)
pred_nbeats_14 = NBEATS_14.predict(n=7,past_covariates=timeseries_flag_future)
| closed | 2024-01-23T00:33:06Z | 2024-02-22T14:41:49Z | https://github.com/unit8co/darts/issues/2186 | [
"question"
] | suswamin | 1 |
ansible/awx | automation | 15,088 | Build of 24.2.0 image failed | ### Please confirm the following
- [X] I agree to follow this project's [code of conduct](https://docs.ansible.com/ansible/latest/community/code_of_conduct.html).
- [X] I have checked the [current issues](https://github.com/ansible/awx/issues) for duplicates.
- [X] I understand that AWX is open source software provided for free and that I might not receive a timely response.
- [X] I am **NOT** reporting a (potential) security vulnerability. (These should be emailed to `security@ansible.com` instead.)
### Bug Summary
Build and push of the 24.2.0 release image failed 2 hours ago:
https://github.com/ansible/awx/actions/runs/8619697870/job/23626024956#step:8:147
And operator version 2.15.0 is already depending on that image tag:
https://github.com/ansible/awx-operator/releases/tag/2.15.0
Do we have an ETA on when the build job will be retriggered? Thanks
### AWX version
24.2.0
### Select the relevant components
- [ ] UI
- [ ] UI (tech preview)
- [ ] API
- [ ] Docs
- [X] Collection
- [ ] CLI
- [X] Other
### Installation method
kubernetes
### Modifications
no
### Ansible version
_No response_
### Operating system
_No response_
### Web browser
_No response_
### Steps to reproduce
Deploy AWX using the AWX operator
### Expected results
Image can be pulled
### Actual results
Image can't be pulled since tag doesn't exist
### Additional information
_No response_ | closed | 2024-04-09T19:56:03Z | 2024-04-10T16:34:12Z | https://github.com/ansible/awx/issues/15088 | [
"type:bug",
"component:awx_collection",
"needs_triage",
"community"
] | Nachichuri | 9 |
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